www.interviewkickstart.com
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Submitted URL: http://www.interviewkickstart.com/
Effective URL: https://www.interviewkickstart.com/
Submission: On January 19 via manual from CA — Scanned from CA
Effective URL: https://www.interviewkickstart.com/
Submission: On January 19 via manual from CA — Scanned from CA
Form analysis 6 forms found in the DOM
Name: wf-form-Webinar-Registration-Part-1 — GET
<form id="wf-form-Webinar-Registration-Part-1" name="wf-form-Webinar-Registration-Part-1" data-name="Webinar Registration Part 1" method="get" class="webinar__registration-form1" data-wf-page-id="653b9c0abfa9142eb0846d44"
data-wf-element-id="2a5c101b-ea03-3bc9-e707-a7a615756e3d" aria-label="Webinar Registration Part 1">
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<input type="hidden" value="How to Nail your next Technical Interview" name="Event Name">
<input type="hidden" value="America/Vancouver" name="user_timezone" class="user_timezone">
<input type="hidden" value="https://www.interviewkickstart.com/" name="page_url" class="page_url">
<input type="hidden" value="" name="site_url" class="site_url">
<input type="hidden" value="Canada" name="country" class="v_country">
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<input type="hidden" value="No" name="Is it exit intent popup?" class="is_exit_intent_popup">
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<div class="webinar__registration-step1">
<div class="form-stages-block">
<div class="step__card active">
<div class="steps__number">
<div>1</div>
</div>
<div>Enter details</div>
</div>
<div class="arrow-lcon"></div>
<div class="step__card">
<div class="steps__number">
<div>2</div>
</div>
<div>Select webinar slot</div>
</div>
</div>
<div class="w-layout-grid webinar__lightbox--form-grid">
<div id="w-node-_2a5c101b-ea03-3bc9-e707-a7a615756e4f-15756e2d" class="webinar_field-block">
<div class="form-label-row"><label for="First-Name" class="webinar_filed-text">First Name</label>
<div class="form-invalid-msg first-name-error hide">*Invalid Name</div>
</div><input class="webinar_field-input first-name w-input" autocomplete="on" maxlength="256" name="First-Name" data-name="First Name" placeholder="Enter first name" type="text" id="First-Name">
</div>
<div id="w-node-_1e1724a1-f571-362a-1d27-81e2d16d3a11-15756e2d" class="webinar_field-block">
<div class="form-label-row"><label id="Last-Name-2" for="First-Name" class="webinar_filed-text">Last Name</label>
<div class="form-invalid-msg last-name-error hide">*Invalid Name</div>
</div><input class="webinar_field-input last-name w-input" maxlength="256" name="Last-Name" data-name="Last Name" placeholder="Enter last name" type="text" id="Last-Name">
</div>
</div>
<div class="webinar_field-block">
<div class="form-label-row"><label id="Email-Address" for="Email-Address-3" class="webinar_filed-text">Email Address</label>
<div class="form-invalid-msg email-id-error hide">*Invalid Email Address</div>
</div><input class="webinar_field-input email w-input" maxlength="256" name="Email-Address" data-name="Email Address" placeholder="Enter email" type="email" id="Email-Address-3">
</div><input type="submit" data-wait="Please wait..." class="bc__btn-select-webinar-slot-v2 w-button" value="Select your webinar time">
<div class="webinar-lighbox-small-text">By sharing your contact details, you agree to our <a href="/privacy-policy" class="link_privacy_policy">privacy policy.</a></div>
</div>
</form>Name: wf-form-Webinar-Registration-Part-2 — GET
<form id="Webinar-Registration-Part-2" name="wf-form-Webinar-Registration-Part-2" data-name="Webinar Registration Part 2" method="get" class="webinar__registration-form2" data-wf-page-id="653b9c0abfa9142eb0846d44"
data-wf-element-id="2a5c101b-ea03-3bc9-e707-a7a615756e88" aria-label="Webinar Registration Part 2">
<div class="webinar__registration-step2">
<div class="form-stages-block">
<div class="step__card completed">
<div class="steps__number completed">
<div class="step__text-hide">1</div>
</div>
<div>Enter details</div>
</div>
<div class="arrow-lcon"></div>
<div class="step__card active">
<div class="steps__number">
<div>2</div>
</div>
<div>Select webinar slot</div>
</div>
</div>
<div class="w-embed"><input type="hidden" value="NoPhoneInTheFirstStep" name="ByeCalendlyType" class="bye-calendly-type">
<input type="hidden" value="" name="First Name" class="wr__firstname">
<input type="hidden" value="" name="Last Name" class="wr__lastname">
<input type="hidden" value="" name="Email Address" class="wr__email">
<input type="hidden" value="" name="Phone Number" class="wr__phone">
<input type="hidden" value="REGULAR" name="webinar-type" class="webinar-type">
<input type="hidden" value="" name="Webinar Lead Type" class="webinar-lead-type">
<input type="hidden" value="Toronto" name="City" class="wr__city">
<input type="hidden" value="Desktop" name="Device" class="wr__device">
<input type="hidden" value="Ontario" name="Region" class="wr__region">
<input type="hidden" value="" name="Referrer" class="wr__referrer">
<input type="hidden" value="" name="Event Start Time" class="wr__event-start-time">
<input type="hidden" value="" name="Event End Time" class="wr__event-end-time">
<input type="hidden" value="" name="Invitee Start Time" class="wr__invitee-start-time">
<input type="hidden" value="" name="Invitee End Time" class="wr__invitee-end-time">
<input type="hidden" value="How to Nail your next Technical Interview" name="Event Name">
<input type="hidden" value="9e1d794d-3685-453b-b1ca-221c16dd9379-1705663474003" name="User ID" class="user_id">
<input type="hidden" value="ik.com/" name="CTA Page" class="cta_page_url">
<input type="hidden" value="ik.com" name="Landing Page" class="l_page_url">
<input type="hidden" value="Organic" name="utm_source" class="utm_source">
<input type="hidden" value="" name="utm_medium" class="utm_medium">
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<input type="hidden" value="" name="msclkid" class="msclkid">
<input type="hidden" value="" name="fbclid" class="fbclid">
<input type="hidden" value="America/Vancouver" name="user_timezone" class="user_timezone">
<input type="hidden" value="https://www.interviewkickstart.com/" name="page_url" class="page_url">
<input type="hidden" value="" name="site_url" class="site_url">
<input type="hidden" value="Canada" name="country" class="v_country">
<input type="hidden" value="" name="salesforce_uuid" class="salesforce_uuid">
<input type="hidden" value="" name="phone_number_full" class="tno1">
<input type="hidden" value="No" name="Is it exit intent popup?" class="is_exit_intent_popup">
</div>
<div class="webinar__slots"><label class="select-webinar-slot w-radio"><input type="radio" name="start-date" value="2024-01-19T19:30:00-05:00" data-endtime="2024-01-19T20:30:00-05:00" data-invitee_starttime="07:30PM - Friday, January 19, 2024"
data-invitee_endtime="08:30PM - Friday, January 19, 2024" data-name="2024-01-19T19:30:00-05:00" class="w-form-formradioinput select-webinar-radio-btn w-radio-input" data-webinar_lead_type="REGULAR"><span class="w-form-label"
for="start-date-0">Friday, 19 January 2024 | 07:30 PM</span></label><label class="select-webinar-slot w-radio"><input type="radio" name="start-date" value="2024-01-21T20:30:00-05:00" data-endtime="2024-01-21T21:30:00-05:00"
data-invitee_starttime="08:30PM - Sunday, January 21, 2024" data-invitee_endtime="09:30PM - Sunday, January 21, 2024" data-name="2024-01-21T20:30:00-05:00" class="w-form-formradioinput select-webinar-radio-btn w-radio-input"
data-webinar_lead_type="REGULAR"><span class="w-form-label" for="start-date-1">Sunday, 21 January 2024 | 08:30 PM</span></label><label class="select-webinar-slot w-radio"><input type="radio" name="start-date"
value="2024-01-22T19:30:00-05:00" data-endtime="2024-01-22T20:30:00-05:00" data-invitee_starttime="07:30PM - Monday, January 22, 2024" data-invitee_endtime="08:30PM - Monday, January 22, 2024" data-name="2024-01-22T19:30:00-05:00"
class="w-form-formradioinput select-webinar-radio-btn w-radio-input" data-webinar_lead_type="REGULAR"><span class="w-form-label" for="start-date-2">Monday, 22 January 2024 | 07:30 PM</span></label><label
class="select-webinar-slot w-radio"><input type="radio" name="start-date" value="2024-01-23T20:30:00-05:00" data-endtime="2024-01-23T21:30:00-05:00" data-invitee_starttime="08:30PM - Tuesday, January 23, 2024"
data-invitee_endtime="09:30PM - Tuesday, January 23, 2024" data-name="2024-01-23T20:30:00-05:00" class="w-form-formradioinput select-webinar-radio-btn w-radio-input" data-webinar_lead_type="REGULAR"><span class="w-form-label"
for="start-date-3">Tuesday, 23 January 2024 | 08:30 PM</span></label><label class="select-webinar-slot w-radio"><input type="radio" name="start-date" value="2024-01-24T19:30:00-05:00" data-endtime="2024-01-24T20:30:00-05:00"
data-invitee_starttime="07:30PM - Wednesday, January 24, 2024" data-invitee_endtime="08:30PM - Wednesday, January 24, 2024" data-name="2024-01-24T19:30:00-05:00" class="w-form-formradioinput select-webinar-radio-btn w-radio-input"
data-webinar_lead_type="REGULAR"><span class="w-form-label" for="start-date-4">Wednesday, 24 January 2024 | 07:30 PM</span></label><label class="select-webinar-slot w-radio"><input type="radio" name="start-date"
value="2024-01-25T20:30:00-05:00" data-endtime="2024-01-25T21:30:00-05:00" data-invitee_starttime="08:30PM - Thursday, January 25, 2024" data-invitee_endtime="09:30PM - Thursday, January 25, 2024" data-name="2024-01-25T20:30:00-05:00"
class="w-form-formradioinput select-webinar-radio-btn w-radio-input" data-webinar_lead_type="REGULAR"><span class="w-form-label" for="start-date-5">Thursday, 25 January 2024 | 08:30 PM</span></label></div>
<div class="timezone-disclaimer">*All webinar slots are in the <span class="var_localtimezone">US/Pacific</span> timezone</div>
<div class="second-btn_block"><input type="submit" data-wait="Please wait..." class="bc__btn-2nd-step w-button" value="Finish"><a href="#" class="btn-back-to-step1">Back</a></div>
</div>
</form>Name: wf-form-Coding-Experience-Experiment — GET
<form id="wf-form-Coding-Experience-Experiment" name="wf-form-Coding-Experience-Experiment" data-name="Coding Experience Experiment" method="get" class="coding-experience-experiment" data-wf-page-id="653b9c0abfa9142eb0846d44"
data-wf-element-id="61c4c4c3-9c75-8c5f-4bc7-e316001590dd" aria-label="Coding Experience Experiment">
<div class="w-embed"><input type="hidden" value="Organic" name="utm_source" class="utm_source">
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<input type="hidden" value="" name="utm_campaign" class="utm_campaign">
<input type="hidden" value="" name="utm_content" class="utm_content">
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<input type="hidden" value="" name="msclkid" class="msclkid">
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<input type="hidden" value="9e1d794d-3685-453b-b1ca-221c16dd9379-1705663474003" name="User ID" class="user_id">
<input type="hidden" value="ik.com/" name="CTA Page" class="cta_page_url">
<input type="hidden" value="ik.com" name="Landing Page" class="l_page_url">
<input type="hidden" value="America/Vancouver" name="user_timezone" class="user_timezone">
<input type="hidden" value="https://www.interviewkickstart.com/" name="page_url" class="page_url">
<input type="hidden" value="" name="site_url" class="site_url">
<input type="hidden" value="Canada" name="country" class="v_country">
<input type="hidden" value="" name="salesforce_uuid" class="salesforce_uuid">
</div><label class="form-radio w-radio">
<div class="w-form-formradioinput w-form-formradioinput--inputType-custom exp-custom-radio w-radio-input"></div><input id="No-coding-experience-Currently-studying" type="radio" name="years-of-experience" data-name="years-of-experience"
style="opacity:0;position:absolute;z-index:-1" value="No coding experience/Currently studying"><span class="radio-button-label-2 w-form-label" for="No-coding-experience-Currently-studying">No coding experience/Currently studying</span>
</label><label class="form-radio w-radio">
<div class="w-form-formradioinput w-form-formradioinput--inputType-custom exp-custom-radio w-radio-input"></div><input id="0-4-years-2" type="radio" name="years-of-experience" data-name="years-of-experience"
style="opacity:0;position:absolute;z-index:-1" value="0-4 years"><span class="radio-button-label-2 w-form-label" for="0-4-years-2">0-4 years</span>
</label><label class="form-radio w-radio">
<div class="w-form-formradioinput w-form-formradioinput--inputType-custom exp-custom-radio w-radio-input"></div><input id="4-years" type="radio" name="years-of-experience" data-name="years-of-experience"
style="opacity:0;position:absolute;z-index:-1" value="4+ years"><span class="radio-button-label-2 w-form-label" for="4-years">4+ years</span>
</label>
</form>Name: wf-form-Exit-Intent-Free-Course-Offering — GET
<form id="wf-form-Exit-Intent-Free-Course-Offering" name="wf-form-Exit-Intent-Free-Course-Offering" data-name="Exit Intent Free Course Offering" method="get" class="exit-intent-free-course-form" data-wf-page-id="653b9c0abfa9142eb0846d44"
data-wf-element-id="ccbb98f2-4ba5-ca7b-756b-f63678e9d0ae" aria-label="Exit Intent Free Course Offering">
<div>
<h2 class="bc-popup-title t-green-2 text-green tale-green _4"><strong>FREE course on 'Sorting Algorithms'</strong> by <span class="text-span-2"><strong>Omkar Deshpande <em class="omkar-designation">(Stanford PhD, Head of Curriculum,
IK)</em></strong></span></h2>
<div class="w-embed"><input type="hidden" value="Organic" name="utm_source" class="utm_source">
<input type="hidden" value="" name="utm_medium" class="utm_medium">
<input type="hidden" value="" name="utm_campaign" class="utm_campaign">
<input type="hidden" value="" name="utm_content" class="utm_content">
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<input type="hidden" value="" name="msclkid" class="msclkid">
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<input type="hidden" value="9e1d794d-3685-453b-b1ca-221c16dd9379-1705663474003" name="User ID" class="user_id">
<input type="hidden" value="ik.com/" name="CTA Page" class="cta_page_url">
<input type="hidden" value="ik.com" name="Landing Page" class="l_page_url">
<input type="hidden" value="America/Vancouver" name="user_timezone" class="user_timezone">
<input type="hidden" value="https://www.interviewkickstart.com/" name="page_url" class="page_url">
<input type="hidden" value="" name="site_url" class="site_url">
<input type="hidden" value="Canada" name="country" class="v_country">
<input type="hidden" value="" name="salesforce_uuid" class="salesforce_uuid">
</div>
</div>
<div class="form-fields-row"><input class="form-input-field mb-0 exitintent-fc-email w-input" maxlength="256" name="Email-Address" data-name="Email Address" placeholder="Enter your email id" type="email" id="Email-Address-6" required=""><input
type="submit" data-wait="Please wait..." class="btn-getaccess w-button" value="Get Access Now"></div>
</form>Name: wf-form-Download-Course-Brochure---Organic-LPs — GET
<form id="wf-form-Download-Course-Brochure---Organic-LPs" name="wf-form-Download-Course-Brochure---Organic-LPs" data-name="Download Course Brochure - Organic LPs" method="get" class="form__download-brochure" data-wf-page-id="653b9c0abfa9142eb0846d44"
data-wf-element-id="cf66683e-86b7-31b5-3aff-2e9f8e17b603" aria-label="Download Course Brochure - Organic LPs">
<div class="w-embed"><input type="hidden" class="utm_source" id="utm_source" name="utm_source" value="Organic">
<input type="hidden" class="utm_medium" id="utm_medium" name="utm_medium" value="">
<input type="hidden" class="utm_campaign" id="utm_campaign" name="utm_campaign" value="">
<input type="hidden" class="utm_adset" id="utm_adset" name="utm_adset" value="">
<input type="hidden" class="utm_content" id="utm_content" name="utm_content" value="">
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<input type="hidden" class="user_timezone" id="user_timezone" name="user_timezone" value="America/Vancouver">
<input type="hidden" class="page_url" id="page_url" name="page_url" value="https://www.interviewkickstart.com/">
<input type="hidden" class="site_url" id="site_url" name="site_url" value="">
<input type="hidden" class="v_country" id="v_country" name="v_country" value="Canada">
<input type="hidden" class="" id="" name="Course" value="Machine Learning / AI">
</div>
<div class="form__step1">
<h3 class="form-card__title">Download the Machine Learning<br>Course Brochure</h3>
<p class="form-card__sub-text">Get all the details about the course & pricing.</p>
<div class="w-layout-grid grid">
<div id="w-node-cf66683e-86b7-31b5-3aff-2e9f8e17b60d-b0846d44"><label for="First-Name-9" class="form-label-1">First name <span class="fname-error hide">*required</span></label><input class="form-input first-name w-input" maxlength="256"
name="First-Name-2" data-name="First Name 2" placeholder="First name" type="text" id="First-Name-9"></div>
<div id="w-node-cf66683e-86b7-31b5-3aff-2e9f8e17b613-b0846d44"><label for="Last-Name-9" class="form-label-1">Last name <span class="lname-error hide">*required</span></label><input class="form-input last-name w-input" maxlength="256"
name="Last-Name-2" data-name="Last Name 2" placeholder="Last name" type="text" id="Last-Name-9"></div>
<div id="w-node-cf66683e-86b7-31b5-3aff-2e9f8e17b619-b0846d44"><label for="Phone-Number" class="form-label-1">Phone number <span class="mobile-error hide">*required</span></label><input class="form-input phone w-input" maxlength="256"
name="Phone-Number" data-name="Phone Number" placeholder="Enter contact number" type="text" id="Phone-Number"></div>
<div id="w-node-cf66683e-86b7-31b5-3aff-2e9f8e17b61f-b0846d44"><label for="Email-Address-7" class="form-label-1">Email address <span class="email-error hide">*required</span></label><input class="form-input email w-input" maxlength="256"
name="Email-Address-7" data-name="Email Address 7" placeholder="Enter email address" type="text" id="Email-Address-7">
<div class="spacer _32"></div><label class="w-checkbox checkbox-field mr-20"><input id="checkbox-2" type="checkbox" name="checkbox-2" data-name="Checkbox 2" class="w-checkbox-input" checked=""><span class="check-box-text w-form-label"
for="checkbox-2">By providing your contact information you agree to our <a href="#" target="_blank">Privacy Policy</a></span></label>
<div class="text-center"><a data-cta-id="download_course_brochure_step_1" href="#" class="form-cta btn-download-brochure w-button">Download Brochure</a>
<div class="spacer-3 _16"></div>
</div>
</div>
</div>
</div>
<div class="form__step2">
<h3 class="form-card__title">Almost there...</h3>
<div class="spacer _20"></div>
<div class="w-layout-grid grid">
<div id="w-node-cf66683e-86b7-31b5-3aff-2e9f8e17b635-b0846d44"><label for="Work-Experience" class="form-label-1">Your work experience (in years)</label><select id="Work-Experience" name="Work-Experience" data-name="Work Experience"
class="form-select-2 w-select">
<option value="">Select one...</option>
<option value="0-2">0-2</option>
<option value="3-8">3-8</option>
<option value="9-15">9-15</option>
<option value="16-20">16-20</option>
<option value="20+">20+</option>
</select></div>
<div id="w-node-cf66683e-86b7-31b5-3aff-2e9f8e17b639-b0846d44"><label for="Domain-Role" class="form-label-1">Your domain/role</label><select id="Domain-Role" name="Domain-Role" data-name="Domain Role" class="form-select-2 w-select">
<option value="">Select one...</option>
<option value="Back-end">Back-end</option>
<option value="Cloud Engineer">Cloud Engineer</option>
<option value="Cyber Security">Cyber Security</option>
<option value="Data Engineer">Data Engineer</option>
<option value="Data Science">Data Science</option>
<option value="Front-end">Front-end</option>
<option value="Full Stack">Full Stack</option>
<option value="Machine Learning / AI">Machine Learning / AI</option>
<option value="Engineering Manager - any domain">Engineering Manager - any domain</option>
<option value="Product Manager (Tech)">Product Manager (Tech)</option>
<option value="Technical Program Manager">Technical Program Manager</option>
<option value="Test Engineer / SDET / QE">Test Engineer / SDET / QE</option>
<option value="Android Developer">Android Developer</option>
<option value="iOS Developer">iOS Developer</option>
<option value="SRE / DevOps">SRE / DevOps</option>
<option value="Embedded Software Engineer">Embedded Software Engineer</option>
<option value="Other Software Engineers">Other Software Engineers</option>
<option value="No Coding Experience">No Coding Experience</option>
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We use cookies to enable the best possible experience on our site and to optimize the content for you. If you continue surfing you agree. Learn more Got it! Register for our webinar HOW TO NAIL YOUR NEXT TECHNICAL INTERVIEW 1 hour Loading... 1 Enter details 2 Select webinar slot First Name *Invalid Name Last Name *Invalid Name Email Address *Invalid Email Address By sharing your contact details, you agree to our privacy policy. Step 1 Step 2 Congratulations! You have registered for our webinar Oops! Something went wrong while submitting the form. 1 Enter details 2 Select webinar slot Friday, 19 January 2024 | 07:30 PMSunday, 21 January 2024 | 08:30 PMMonday, 22 January 2024 | 07:30 PMTuesday, 23 January 2024 | 08:30 PMWednesday, 24 January 2024 | 07:30 PMThursday, 25 January 2024 | 08:30 PM *All webinar slots are in the US/Pacific timezone Back Step 1 Step 2 Confirmed You are scheduled with Interview Kickstart. Redirecting... Oops! Something went wrong while submitting the form. YOU MAY BE MISSING OUT ON A 66.5% SALARY HIKE* I want my career success nowI want to delay my success NICK CAMILLERI Head of Career Skills Development & Coaching *Based on past data of successful IK students Help us know you better! HOW MANY YEARS OF CODING EXPERIENCE DO YOU HAVE? No coding experience/Currently studying 0-4 years 4+ years Thank you! Your submission has been received! Oops! Something went wrong while submitting the form. FREE COURSE ON 'SORTING ALGORITHMS' BY OMKAR DESHPANDE (STANFORD PHD, HEAD OF CURRICULUM, IK) Enter valid email id Thank you! Please check your inbox for the course details. Oops! Something went wrong while submitting the form. HOW CAN WE HELP? Interview Kickstart has enabled over 3500 engineers to uplevel. REGISTER FOR WEBINAR Our founder takes you through how to Nail Complex Technical Interviews. READ OUR REVIEWS Our alumni credit the Interview Kickstart programs for their success. SEND US A NOTE One of our Program Advisors will get back to you ASAP. USA's #1 Tech Interview Prep Platform is now in India! Visit the India site NEXT WEBINAR STARTS IN 00 Day : 13 Hrs : 05 Mins : 15 Secs Join the webinar Join the webinar About usWhy usInstructorsReviewsCostFAQContactBlogRegister for Webinar Accelerate Your Tech Career: Select the best path for you Interview Prep for top tech companies Switch to Machine Learning/Data Science NAIL YOUR NEXT TECHNICAL INTERVIEW ... by becoming a better engineer Learn how to boost your interview success rate Join the webinar Join the webinar Register for our webinar Register for our webinar NEXT WEBINAR STARTS IN 00 Day : 13 HRS : 05 Mins : 15 secs WHY CHOOSE INTERVIEW KICKSTART? Here's the TL;DR version LIVE CLASSES BY OVER 500 FAANG+ HIRING MANAGERS $312K AVERAGE SALARY FOR SDES, EMS, TPMS, PMS, DATA ANALYSTS SALARY HIKES OF $100K - $150K 8 YRS IN BUSINESS, THOUSANDS OF FAANG+ OFFERS 100% MONEYBACK GUARANTEE Register for webinar It's Free NEXT WEBINAR STARTS IN 00 Day : 13 Hrs : 05 Mins : 15 Secs Top companies love hiring our candidates Top companies hiring now DON’T WAIT! February 2024 COHORTS GOING FAST January 2024 COHORTS ENROLLMENTS CLOSED PRIOR COHORTS OVERSUBSCRIBED 500+ Instructors, Coaches & Interviewers from Top Tech Companies 17,000+ Students 18 Highest number of offers received by an IK alum $1.2M Highest compensation received by our Alum $312,275 Average value of offers received by alums 66.5% Avg. salary hike for alums who upleveled INSTRUCTORS AND MENTORS FROM FAANG & TIER-1 COMPANIES YUVAL SCHARF Software Engineer ADRIAN FERNANDEZ Engineering Manager QIUPING X Principal Scientist Manager ZHUANG LIANG Director Of Engineering OMKAR DESHPANDE Head of Technical Curriculum Slide 3 of 5. YUVAL SCHARF Software Engineer ADRIAN FERNANDEZ Engineering Manager QIUPING XU Principal Scientist Manager ZHUANG LIANG Director Of Engineering NICK CAMILLERI Head of Career Skills Development and Coaching OMKAR DESHPANDE Head of Technical Curriculum Meet your instructors PICK A PROGRAM THAT SUITS YOUR GOAL STEPUP Accelerated interview prep to step up into a Tier-1 company < 2 Months to prepare Self-paced course 10 mentor sessions/mock interviews Customizable Placement assistance Unlimited coaching sessions Visa advice Learn more LEVELUP Guided interview prep to level up into a Tier-1 company 3+ Months to prepare POPULAR Instructor-led live course 15-21 mentor sessions/mock interviews Customizable Placement assistance Unlimited coaching sessions Visa advice Learn more SWITCHUP Upskill and switch to a new role at a Tier-1 tech company 11+ Months to prepare Instructor-led live course 15 mentor sessions/mock interviews Available for Data Science and ML Engineers Placement assistance Unlimited coaching sessions Visa advice Choose domain Become an AI/ML Data ScientistBecome an AI/ML Engineer 18 LEVELUP COURSES FOR KEY TECH ROLES Includes domain training, coding, systems design, behavioral interview prep, mock interviews & lifelong learning. Back-end EngineeringFull Stack EngineeringFront-end EngineeringEngineering ManagerEarly EngineeringEmbedded SystemsMachine LearningData EngineeringSite Reliability Engineering iOS EngineeringAndroid EngineeringTest EngineeringTechnical Program ManagerData ScienceProduct Manager (Tech)Security EngineeringAWS Cloud Solutions ArchitectData Analyst & Business Analyst Not sure which course to select? No problem - you can change your course anytime during the first 3 weeks SOFTWARE COURSES Back-end Engineering Full Stack Engineering Front-end Engineering Test Engineering iOS Engineering Android Engineering Early Engineering TECH MANAGEMENT COURSES Engineering Manager Technical Program Manager Product Manager (Tech) DATA COURSES Machine Learning Data Engineering Data Science Data Analyst & Business Analyst SYSTEMS COURSES Embedded Systems AWS Cloud Solutions Architect Site Reliability Engineering Cyber Security Not sure which course to select? No problem - you can change your course anytime during the first 3 weeks To learn more about the Courses Register for our Webinar Register for our Webinar NEXT WEBINAR STARTS IN 00 Day : 13 Hrs : 05 Mins : 15 Secs INTERVIEWING IS A SKILL Top tech companies receive thousands of applications. To identify the best candidates, their interviews are designed to be extremely challenging. You * Are expected to prepare for the interview. * Are tested on core DSA, system design, and behavioral skills, in addition to your domain. * Have to come up with solutions to complex problems in a short span of time. * Are asked to articulate your assumptions, your decisions and your solutions. IK PREP - DESIGNED FOR SUCCESS COMPREHENSIVE CURRICULUM DSA, System Design, Domain Concepts, Career Skills, and more RIGOROUS MOCK INTERVIEWS With actual Hiring Managers. Get Detailed Feedback, Scores and Reference Videos PLENTY OF 1 X 1 HELP Technical Coaching, Homework Assistance, Solutions Discussion and Individual sessions PERSONALIZED FEEDBACK Brutally honest and structured - the kind that actually helps CAREER SKILLS DEVELOPMENT Resume Help, LinkedIn Profile Feedback, Personal Branding and Live Workshops SALARY NEGOTIATION Company-, role-, and level-specific help based on real data from instructors and students DESIGNED FOR WORKING PROFESSIONALS EVENINGS & WEEKENDS Intense, but designed to fit into your work and life schedule. REMOTE Participate in live classes remotely. LONG SUPPORT PERIOD To help you catch up with everything that we offer. GET ACCESS TO UPLEVEL WHEN YOU ENROLL IN A COURSE In-browser online judge, mock interview suite, on-demand timed tests, and more to add structure to your interview prep journey Learn more TOP OFFERS FROM THE BEST COMPANIES From Entry Level to Directorial Level ... and many more. TESTIMONIALS Our alums talk about how IK helped them succeed Each instructor-led session was packed with information and there were lots of problems to practice. The course was intense, but it was a great use of my time. Neelesh Tendulkar Offers Google, Intuit Interview Kickstart is like a fitness coach which guides to achieve your dream job. It can help you identify your weak points and also suggest steps to improve them. Swapnil Tailor Offers Facebook, Twitter, Linkedin The classes, workshops, quizzes, practice problems, and mock interviews provided me with the knowledge, tools, and the feedback that I was missing. Interview Kickstart showed me how to prepare for success. Flavia Vela Offers LinkedIn, Amazon I can't think of a better recipe for tech interview success than combining the Interview Kickstart program with hard work. The program made my prep much more effective and eliminated surprises from the interview process. Michael Huston Offers Databricks, Amazon, Airbnb IK provides a nice, structured way to prepare for interviews while having a full-time job. Mock interviews helped me get better and the problem sets alleviated the need for me to source problems externally. Kushal L Offers Facebook “The course was very intense. During the two months it lasted, I would easily work 2+ hours every day, weekends included, on the homework problems. This course is just practice, practice, practice. And it works! Fast forward a couple of weeks, and I accepted my offer with Facebook.” Davide Testuggine Offers Facebook Slide 1 of 2. Read more reviews OUR METHODOLOGY WORKS Our courses constantly evolve based on industry trends, instructor insights, and feedback from students SOLVE UNSEEN PROBLEMS Recognize patterns in interview problems and rehearse them until you feel prepared. OVERCOME INTERVIEW ANXIETY You're good at what you do, but anxiety kills your interviews. Get over it with prolific practice. 50% MONEY-BACK GUARANTEE* If you do well in our StepUp and LevelUp programs but still don't land a domain-relevant job within the post-program support period, we'll refund 50% of the tuition you paid for the course.* READY TO ENROLL? Get your enrollment process started by registering for a Pre-enrollment Webinar with one of our Founders. Register for our Webinar Register for our Webinar NEXT WEBINAR STARTS IN 00 Day : 13 HRS : 05 Mins : 15 secs ABOUT US Interview Kickstart, established in 2014, is the gold standard for Technical Interview Preparation. Our 500+ instructors, drawn from tech giants like Google and Amazon, have guided 17,000+ engineers beyond skill enhancement- from mock interviews and real-world projects to effective salary negotiation. The outcome? Alumni landing jobs with $300K+ offers, and the highest compensation at a whopping $1.28 Million. Switchup: TRANSFORM YOUR CAREER TRANSITION TO AI/ MACHINE LEARNING ENGINEERING ROLES AT TIER-1 COMPANIES 4.65 Students enrolled: 240 Designed and taught by FAANG+ AI/Machine Learning Engineers to help you transform your career and land your dream job. ML Engineers! Get interview-ready with lessons from FAANG+ experts Master core Machine Learning concepts Sharpen your coding and system design skills Machine Learning Register for webinar Learn more about the course & pricing It's Free NEXT WEBINAR STARTS IN 00 Day : 13 Hrs : 05 Mins : 15 Secs Course Overview Start Learning Get all the information about the course and pricing in our live webinar with Q&A. Download Course Brochure Almost full Next Batch 12th June, 2022 Location Live & online Duration 4 months (apx. 10 hours/week) CurriculumMeet the instructorsTypical Week at IKMock interviewsInterview Prep GuideCareer Impact CurriculumMeet the instructorsTypical Week at IKMock interviewsInterview Prep GuideCareer Impact STUDENTS WHO CHOSE TO UPLEVEL WITH IK GOT PLACED AT Siva Karthik Gade SDE, Machine Learning Sai Marapa Reddy SWE, Machine Learning Safir Merchant SWE, Machine Learning Jameson Merkow Principal AI Engineer Sayan Banerjee Data Scientist II Manika Kapoor Senior Deep Learning Scientist Mike Kane Lead Data Engineer, Analytics Akshay Lodha Data Engineering & Analytics Anju Mercian Data Engineering Consultant Alokkumar Roy Data Engineer 17,000+ Tech professionals trained $1.2M Highest offer received by an IK alum 53% Average salary hike received by alums AI/MACHINE LEARNING COURSE CURRICULUM PART 1: MASTERING MACHINE LEARNING Foundations 2 months 5 live classes 1 PYTHON FUNDAMENTALS * Variables, if-else, loops, Functions, lists, strings, etc. related coding examples * Tuples, Set, Dict, map, filter, reduce etc. related coding examples * OOP in Python, File and Exception handling * Numpy, Pandas, etc. * Visualisation with Python 2 SOFTWARE DEVELOPMENT ESSENTIALS * Scripting, Git & GitHub * Client-Server Architecture, HTTP & REST APIs * Databases & Intro to SQL Essential Mathematics for Machine Learning 2 months 3 live classes 1 ESSENTIALS OF PROBABILITY 2 PROBABILITY DISTRIBUTION 3 ESSENTIALS OF STATISTICS 4 HYPOTHESIS TESTING 5 BASIC AND LINEAR ALGEBRA 6 CALCULUS 7 VECTORS AND MATRICES 8 REGRESSION Deep Dive into Machine Learning Engineering 6 months 5 live classes 1 FOUNDATIONAL MACHINE LEARNING CONCEPTS * Data Pipelines for ML: Techniques and Best Practices * Supervised Machine Learning Techniques and Applications * Unsupervised Machine Learning Techniques and Applications * Introduction to Neural Networks and Deep Learning Architectures such as RNN, LSTM, CNN etc * AI Development through Multiple Mini Projects: Hands-on Techniques and Applications 2 ADVANCED MACHINE LEARNING FRAMEWORK: TECHNIQUES AND BEST PRACTICES FOR SUCCESSFUL DEVELOPMENT * NLP: Techniques and Applications of Natural Language Processing using Embeddings, Autoencoders, VAE, GANs etc. * Generative AI : BERT, Transformers, and LLMs for Advanced AI Development * Computer Vision Techniques and Applications in AI for Image and Video Analysis : Object Detection, Boundary Detection, Image Segmentation etc * Reinforcement Learning through Human Feedback (RLHF) : Introduction and Applications in Generative AI. * Deep Learning Mini Projects: Hands-on Techniques and Applications to build a system from scratch. 3 ML DEVELOPMENT AND DEPLOYMENT: ADVANCED ML AND MLOPS TECHNIQUES * Software System Design Fundamentals: Principles and Best Practices for AI-based Development * ML Design Principles: Guiding principles for developing effective ML systems. * Scoping ML Projects: Defining goals, objectives, and boundaries of ML projects * Distributed Training: Data & Model Parallelism using GPUs * Model Deployment: Best Practices for Successful Implementation and Maintenance at Scale * Improving Model Performance: Techniques and Strategies for Retraining and Model Decay * AI Model Monitoring and Maintenance: Best Practices for Ensuring Optimal Performance * Failure Analysis: Techniques and Strategies for Diagnosing Production Issues * Ensuring ML Model Stability: Techniques and Best Practices 4 CAPSTONE PROJECT: REAL-WORLD APPLICATIONS AND HANDS-ON EXPERIENCE IN MACHINE LEARNING * Industry-Relevant AI Projects: Techniques and Best Practices for Developing Real-world Solutions * AI Mentorship at FAANG Companies: Techniques and Best Practices for Career Development * Capstone Presentation: Best Practices for Presenting Machine Learning Solutions to Stakeholders Understanding the ML Development Framework 3 weeks 3 live classes 1 BASICS OF SOFTWARE SYSTEM DESIGN 2 ML DESIGN PRINCIPLES 3 ML PROJECT SCOPING 4 ML MODEL DEPLOYMENT Advanced Machine Learning and MLOPs 5 weeks 1 ADVANCED MACHINE LEARNING * Recommendation Systems * Natural Language Processing * Modern ML Architectures 2 MLOPS * Model performance and re-training * Model Monitoring * Diagnosing Production Failures * Model Stability Capstone Project 2 weeks 1 INDUSTRY-RELEVANT PROJECTS, REPLICATING REAL-LIFE PROJECTS AT TIER-1 COMPANIES PART 2: INTERVIEW PREPARATION Data Structures and Algorithms Interview Preparation 4 weeks 5 live classes 1 TREES 2 GRAPHS 3 GREEDY ALGORITHMS 4 DYNAMIC PROGRAMMING Software System Design Interview Preparation 3 weeks 5 live classes 1 ONLINE PROCESSING SYSTEMS 2 BATCH PROCESSING SYSTEMS 3 STREAM PROCESSING SYSTEMS AND OBJECT MODELING AI/Machine Learning Interview Preparation 5 weeks 5 live classes 1 SUPERVISED LEARNING I - RANK RELEVANT SEARCH RESULTS 2 SUPERVISED LEARNING II - DESIGN A YOUTUBE VIDEO RECOMMENDATION SYSTEM 3 UNSUPERVISED LEARNING - DETECT FRAUD TRANSACTIONS FOR AIRBNB 4 DEEP LEARNING I - DETECT AND PROCESS OBJECTS IN A SCENE 5 DEEP LEARNING II - BUILD A TECH SUPPORT CHATBOT 6 ADDITIONAL TOPICS: * Comprehensive, Step-by-Step Approach to ML System Design Interviews * Modern ML Architectures * Reinforcement Learning Career & Behavioral Sessions 3 weeks 5 live classes 1 INTERVIEW STRATEGY AND SUCCESS 2 BEHAVIORAL INTERVIEW PREP 3 OFFERS AND NEGOTIATION Register for webinar It's Free Try 7-Day Email Course for FREE NEXT WEBINAR STARTS IN 00 Day : 13 Hrs : 05 Mins : 15 Secs Best suited for Software Engineers/Developers and Data Science/Engineering professionals who want to move into AI/ML Engineer roles Anyone with basic or no understanding of Machine Learning and looking to master the domain (STEM background required) Recent college graduates/final year undergrads who want to become AI/ML Engineers WHY CHOOSE THIS COURSE? PROGRAM BY FAANG+ ML ENGINEERS 360° course designed and taught by FAANG+ experts to help you become an ML Engineer INDIVIDUALIZED TEACHING AND 1:1 HELP Technical coaching, homework assistance, solutions discussion, and individual sessions CAPSTONE PROJECT Exposure to real-life machine learning projects INTERVIEW PREP MODULES Dedicated interview prep classes focused on helping you get 100% interview-ready MOCK INTERVIEWS WITH FAANG+ ML ENGINEERS Live interview practice in real-life simulated environments with FAANG and top-tier interviewers CAREER SKILLS DEVELOPMENT Resume building, LinkedIn profile optimization, personal branding, and live behavioral workshops Register for webinar It's Free NEXT WEBINAR STARTS IN 00 Day : 13 Hrs : 05 Mins : 15 Secs MEET YOUR INSTRUCTORS Our highly experienced instructors are active hiring managers and employees at FAANG+ companies and know exactly what it takes to ace tech and managerial interviews. MATT NICKENS Manager, Data Science 10+ years experience Learn more Dipan has valuable work experience of over 10 years with companies such as SpaceX. As an operations-focused engineer, he has worked with cross-functional teams such as design, test, production, and supply chain to accomplish mission objectives.As an operations-focused engineer, he has worked with cross-functional teams such as design, test, production, and supply chain to accomplish mission objectives. JAMESON MERKOW Principal AI Engineer 11+ years experience Learn more Dipan has valuable work experience of over 10 years with companies such as SpaceX. As an operations-focused engineer, he has worked with cross-functional teams such as design, test, production, and supply chain to accomplish mission objectives.As an operations-focused engineer, he has worked with cross-functional teams such as design, test, production, and supply chain to accomplish mission objectives. CHRISTIAN MONSON Machine Learning Scientist 9+ years experience Learn more Dipan has valuable work experience of over 10 years with companies such as SpaceX. As an operations-focused engineer, he has worked with cross-functional teams such as design, test, production, and supply chain to accomplish mission objectives.As an operations-focused engineer, he has worked with cross-functional teams such as design, test, production, and supply chain to accomplish mission objectives. ALIREZA DIRAFZOON Research Engineer 7+ years experience Learn more Dipan has valuable work experience of over 10 years with companies such as SpaceX. As an operations-focused engineer, he has worked with cross-functional teams such as design, test, production, and supply chain to accomplish mission objectives.As an operations-focused engineer, he has worked with cross-functional teams such as design, test, production, and supply chain to accomplish mission objectives. BUILT FOR WORKING PROFESSIONALS Here’s what a typical week would look like: SUNDAY 4-hour Live Classes in the morning THURSDAY 2-hour session in the evening to discuss assignments and problem solutions ONCE A WEEK 1-hour technical coaching session to discuss any additional doubts Contact for Pricing PRACTICE AND TRACK PROGRESS ON UPLEVEL UpLevel will be your all-in-one learning platform to get you FAANG-ready, with 10,000+ interview questions, timed tests, videos, mock interviews suite, and more. Mock interviews suite On-demand timed tests In-browser online judge 10,000 interview questions 100,000 hours of video explanations Class schedules & activity alerts Real-time progress update 11 programming languages GET UPTO 15 MOCK INTERVIEWS WITH HIRING MANAGERS What makes our mock Interviews the best: HIRING MANAGERS FROM TIER-1 COMPANIES LIKE GOOGLE & APPLE Interview with the best. No one will prepare you better! DOMAIN-SPECIFIC INTERVIEWS Practice for your target domain - Machine Learning DETAILED PERSONALIZED FEEDBACK Identify and work on your improvement areas TRANSPARENT, NON-ANONYMOUS INTERVIEWS Get the most realistic experience possible More about mock interviews 1. FLEXIBLE SCHEDULE Pick timings convenient to you 4. TECHNICAL AND BEHAVIORAL INTERVIEWS Uplevel your technical and behavioral interview skills 2. REMOTE INTERVIEW EXPERIENCE Mirrors the current format of remote interviews 5. LEVEL-SPECIFIC INTERVIEWS Because an L4 at Google can be quite different from an E7 at Meta 3. FEEDBACK DOCUMENTATION All the feedback you’ve ever wanted, recorded and documented 6. INTERVIEWER OF YOUR CHOICE Choose based on domain CAREER IMPACT Our engineers land high-paying and rewarding offers from the biggest tech companies, including Facebook, Google, Microsoft, Apple, Amazon, Tesla, and Netflix. SIVA KARTHIK GADE SDE — Machine Learning Placed at: IK offers high-quality study material, knowledgeable and patient instructors working at industry-leading companies, well-paced live classes + tests + review sessions, always available technical + career coaches, mock interview support from the best interviewers in the respective fields. IK brings together people with same the ambition (on their platform, UPLEVEL) to guide and inspire each other NADHA GAFUR Machine Learning Engineer Placed at: It has been a great learning experience. The structure is really good and the materials as well. The lectures and live class pre-reading material is very informative and engaging. SAI MARAPA REDDY SWE, Machine Learning Placed at: I completed IK’s program and got offers from a couple of FAANG companies. Why you should take this course: It is well tested and the focus is more on the concepts/templates rather than approaching one problem at a time. You will meet peers who have similar aspirations. You can make groups and help yourselves. JAMESON MERKOW Principal AI Engineer Placed at: I joined IK because I had a lot of really terrible experiences with interviews. The confidence and expertise I routinely demonstrated in the workplace was not translating to interviews. I lacked confidence during behavioral interviews and felt completely lost when asked coding questions. IK taught me how to clearly demonstrate my skills and experience during interviews which ultimately helped me find a Principal engineering position at Microsoft. SAFIR MERCHANT Machine Learning Software Engineer Placed at: I liked the course that IK provided a lot. IK provided all the knowledge on a variety of topics that helped me prepare for coding interviews. The mock interviews were really great. Landing a job at my desired company has been a great pleasure. HOW TO ENROLL FOR THE MACHINE LEARNING COURSE? Learn more about Interview Kickstart and the Machine Learning Course by joining the free pre-enrollment webinar. Register for webinar It's Free Try 7-Day Email Course for FREE NEXT WEBINAR STARTS IN 00 Day : 13 Hrs : 05 Mins : 15 Secs Already preparing or want a sneak peek? Try the ML Interview Prep 7-day email course A FREE GUIDE TO KICKSTART YOUR MACHINE LEARNING CAREER AT FAANG+ From the interview process and career path to interview questions and salary details — learn everything you need to know about Machine Learning careers at top tech companies. Register for webinar It's Free Interview Strategy and Success Interview Questions Career Path Salary and Levels at FAANG Frequently asked questions MACHINE LEARNING INTERVIEW PROCESS OUTLINE Typically, the Machine Learning interview process at FAANG+ and other Tier-1 companies include the following rounds: Initial technical screening * Basic ML understanding, including a discussion on past ML projects * Coding problems 3-8 on-site rounds * 1-2 coding rounds: * Coding problems on Data Structures and Algorithms * ML algorithm coding + project discussion * 1-2 system design rounds: * Scalable/software design * ML Systems * 1-3 ML technical rounds (ML breadth and depth understanding): * ML Algorithms * Deployment Tools and techniques Behavioral round * Open-ended questions to gauge if you're a "good fit” for the company What to Expect at Machine Learning Engineer Interviews 1 Initial phone/technical screening round: This can be a combination of basic ML understanding round/past projects or purely coding-based: Medium Hard LC questions. Some companies refuse to move forward if you fail the initial ML screen. 2 3-8 On-site rounds: * Coding round: This can be a mix of project discussion and coding * System design round: Mix of questions on how to design a general software system * ML system design round (1-2 rounds): For example, design a recommendation system for Netflix. For candidates having less than 3 years of experience, ML system design is often replaced by another core ML Understanding Round of medium to high difficulty * ML fundamental round: Familiarity with algorithms such as Linear/Logistic Regression, Decision Trees, SVM, Deep Neural Networks and optimization techniques, loss functions such as Gradient Descent, Cross-Entropy Loss, etc. These questions can vary based on the specific role and team you are applying for * Behavioral round: You can expect questions on your job experience and discussions on past projects along with open-ended questions to understand if you’re a good fit for the role. For more specific information on the Machine Learning Engineer’s interview process at FAANG+ companies, check out: * Machine Learning Engineering Interviews * Google Machine Learning Engineer Interview Process * Amazon Machine Learning Engineer Interview Process * Apple Machine Learning Engineer Interview Prep 3 Interview Process for Different Machine Learning-Related Roles A typical ML Engineer interview consists of: 1-2 coding rounds – Usually, Data Structures and Algorithms based questions are asked, but some companies also ask you to code basic ML algorithms (Usually in Python) 1-2 system design rounds – One general system design round (like SDE profile) and another ML System design round 1 behavioral round — Questions regarding your past work experience will be asked to see if you’re a cultural fit 1-2 ML fundamentals rounds: These can cover areas such as: * Discussion on past projects in a related field * Understanding of various ML algorithms and their underlying principles * Discussion on challenges and tradeoffs related to each algorithm A typical Applied Scientist interview consists of: 1 coding round – Usually includes questions on Data Structures and Algorithms, but some companies ask to code basic ML algorithms (Python) 1 ML system design round – Mainly focused on ML understanding (compared with the MLE round, where model production and deployment are equally important), i.e., identifying a suitable dataset for the problem, feature engineering, tradeoffs, sampling, etc. 1-2 ML Depth and Breadth rounds: Deep dive into ML fundamentals about their prior experience 1 behavioral round — Questions regarding your past work experience will be asked to see if you’re a cultural fit. A typical Research Scientist interview consists of: 1 coding round – Usually Python library-based (Pytorch/Tensorflow) or LeetCode Easy in some companies. 1 ML problem-solving round – Identifying a suitable dataset for the problem, feature engineering, tradeoffs, experimentation design, how to establish a baseline, modifying current algorithms to suit the situation, etc. 1 presentation round – Present some research problem (from the Ph.D. thesis, previous work experience, or any new topic relevant to the interviewing team), followed by QnAs. Expected to have a firm grasp of Concepts and Advancements in the given problem to answer applied questions. 1-2 ML Depth and Breadth rounds – Deep dive into ML fundamentals about their prior experience. Expected to have proficiency in ML Algorithms from the mathematical to the application level. 1 behavioral round — Questions regarding your past work experience will be asked to see if you’re a cultural fit. For more information on the interview process, read our blog on Machine Learning Engineering Interviews. MACHINE LEARNING INTERVIEW QUESTIONS The interview process for the various Machine Learning positions is quite rigorous, so you need to be prepared accordingly. To get you started, we've compiled a list of the most frequently asked Machine Learning interview questions and segmented them into different categories. 1 Machine Learning Interview Questions on Coding You are given some corrupted text with all the spaces removed. Implement an algorithm to recover the original text. Given a sorted integer array, find the index of a given number’s first or last occurrence. If the element is not present in the array, report that as well. Given: Two strings, A and B, of the same length n. Find: Whether it’s possible to cut both strings at a common point such that the first part of A and the second part of B form a palindrome. Given a tree, write a function to return the sum of the max-sum path which goes through the root node. Given an infinite chessboard, find the shortest distance for a knight to move from position A to position B. Implement a k-means clustering algorithm with just NumPy and Python built-ins. Given a filter and an image, implement a convolution. Follow up with a given stride length, padding, etc. 2 Machine Learning Interview Questions on System Design Design an application for inventory data management. Write a program to retrieve log data in an optimal way. How would you design a function that schedules jobs on a rack of machines knowing that each job requires a certain amount of CPU & RAM, and each machine has different amounts of CPU & RAM? Design a “Hey Siri” style trigger word detection system. In-flight entertainment systems have a vast library of movies that users can enjoy during their journey. Design a system that recommends a set of movies to watch based on the user's preferences and total flight time. How would you detect fraud or predatory house listings on Airbnb? 3 Machine Learning interview Questions on ML Basics Does the vanishing gradient problem occur closer to the beginning or end of the neural network training process? Explain why XGBoost performs better than SVM. How do you deal with imbalanced data? When using sci kit-learn, do we need to scale our feature values when they vary greatly? How would you select the value of "k" in a k-means algorithm? What is the difference between the normal, soft-margin SVM and SVM with a linear kernel? How would you detect spam emails? What is the best metric for this type of system: precision or recall? What do you mean by a generative model? Which methods can you use to summarize the content of 1000 tweets? What are the different ways of preventing over-fitting in a deep neural network? Explain the intuition behind each. 4 Open-ended Machine Learning Interview Questions According to you, which is the most valuable data in our business? What are your thoughts on our current data process? How can we use your Machine Learning skills to generate revenue? How will you quantify the level of success of the projects you implement? Pick any product or app that you really like and describe how you would improve it. For more such questions, read 50+ Machine Learning Interview Questions and Advanced Machine Learning Interview Questions You Should Practice. MACHINE LEARNING CAREER Machine Learning has changed the face of technology as we know it. Machine Learning adoption results in 3x faster execution and 5x faster decision-making. As a result, not only are ML engineer positions in high demand, with companies willing to pay top dollar for the right engineers, but the responsibilities for these roles have become significantly more diverse. When a company hires ML engineers, it wants candidates who can contribute to innovations that will change the world. 1 Machine Learning Job Roles and Responsibilities Machine Learning Engineers are highly skilled programmers who develop Machine Learning systems for business applications. They scale prototype models to large datasets, deploy them on the cloud or internally, and build end-to-end pipelines to continuously monitor the model performance. The responsibilities of an ML Engineer differ from one company to the next and are frequently determined by the size of the company. In this blog, Machine Learning Engineering Roles — What's the Best Fit for You, you can read about the differences between different ML roles and determine which is the best fit for you. Even though the specific responsibilities of ML Engineers may vary considerably, their key day-to-day jobs may include all or a subset of the following: Design and Develop * Identifying the specifications for a scalable Machine Learning model for a specific business requirement * Extracting critical insights from historical data by leveraging data-wrangling expertise * Analyzing the use cases of ML algorithms and ranking them by their success probability * Finding the best models to balance business requirements and architectural constraints * Designing the high-level architecture required to deploy a production scale model on a given platform * Developing Machine Learning models and tools on petabyte or larger scale datasets Test * Identifying differences in data distribution that could affect model performance in real-world situations * Automating model training and evaluation processes * Addressing various bottlenecks in scaling ML models to real-time customers with minimum latency and high throughput * Collaborating with data scientists and engineers to scale prototype solutions and build extensible tools * Monitoring model performance on different datasets under different architectural constraints * Developing pipelines to process and store big data using Hadoop/Scala/Spark-like technologies Deploy * Designing and implementing APIs, services that host these models, and integrating said services to various endpoints * Leveraging AWS (e.g., Sage Maker, Lambda, etc.), Azure, or Google Cloud Platform with other techniques (e.g., Spark, Python, Java, etc.) to deploy production class ML services * Building resilient and transparent end-to-end pipelines to monitor the quality and performance of Machine Learning models Maintain * Maintaining a highly scalable data and model management infrastructure that supports cutting-edge research * Maintaining core system features, services, and engines * Reviewing existing code for accuracy and consistency with best practices and style guidelines * Contributing to documentation and educational content for knowledge transfer * Triaging and resolving production issues by analyzing the source and impact on architecture, operations, and delivery Improve * Training and retraining ML systems and models as needed * Building a suitable product feature roadmap by collecting current and future requirements * Adapting existing algorithms to make use of parallelized or distributed processing systems (e.g., distributed clusters, multicore SMP, and GPU) * Building prototypes and A/B Test pipelines to evaluate algorithm improvements You will work on more and more of the above tasks as you progress in your career as an ML Engineer. However, if you transition into a managerial role, you can also expect to: * Interact directly with customers to understand their requirements and drive changes to product features * Advise and collaborate with cross-functional teams, including researchers, data scientists, and data engineers, to improve architecture, design, and technical capabilities * Identify new products and opportunities for the company and influence the relevant stakeholders to prioritize their development * Develop and manage metrics, KPIs, and dashboards to improve team efficiency and ensure conformation to best practices * Understand industry-wide trends, and collaborate with industry experts to further organizational goals * Effectively communicate complex features & systems in detail * Mentor and support team members and accelerate their career growth 2 Machine Learning Job Requirements and Skills A robust coding background with experience in infrastructure design and end-to-end ML model deployment In-depth knowledge of various ML techniques, their tradeoffs, their advantages in terms of performance, and intuitive understanding of which technique fulfills the need of the hour Awareness of the latest developments in ML/MLOPs and the ability to iteratively improve model performance Confused between Data Science and Machine Learning? Read Machine Learning vs. Data Science — Which Has a Better Future? 3 Qualifications Required to Become a Machine Learning Engineer Basic Qualifications * Bachelor’s degree or Master’s degree in Computer Science or related field * Experience building large-scale machine-learning infrastructure * Experience with at least one modern language such as Java, C++, or C#, including object-oriented design * Hands-on experience deploying Machine Learning models in production * Experience with Machine Learning techniques such as pre-processing data, training, and evaluation of classification and regression models, and statistical evaluation of experimental data. * 1+ years of experience contributing to new and current systems' architecture and design (architecture, design patterns, reliability, and scaling) Preferred Qualifications * Master's degree in Computer Science or related field * Advanced knowledge of performance, scalability, enterprise system architecture, and engineering best practices * Academic and/or industry experience with one or more domains: computer vision, deep learning, Machine Learning, or large-scale distributed systems Wondering how to list skills on your resume? Read Machine Learning Engineer Resume Guide: Tips, Best Formats, and Sample Included. 4 Machine Learning Career Roadmap In a Tier-1 company, the typical career ladder for the ML role looks like this: MACHINE LEARNING ENGINEER SALARY AND LEVELS AT FAANG+ COMPANIES Before moving on to FAANG+ companies, here are the average salaries of ML engineers based on tenure and level in tech companies: * ML Engineer I / Entry Level (L3) * Years of experience: 0-2 * Compensation: $190K+ * ML Engineer II / ML Scientists (L4) * Years of experience: 2-5 * Compensation: $260K+ * Senior ML Engineers / Applied Scientists / Research Scientists (L5) * Years of experience: 5-8 * Compensation: $360K+ * Staff ML Engineers / Team Leads (L6) * Years of experience: 8-15 * Compensation: $500K+ * Principal ML Engineers / ML Directors (L7) * Years of experience: 15+ * Compensation: $850K+ Facebook Machine Learning Engineer Salary Machine Learning Engineer roles at Facebook are highly rewarding, both in terms of compensation as well as professional growth. The different levels of Machine Learning Engineers at Facebook are: E3 (Associate ML Engineer): This is typically the level at which fresher Machine Learning Engineers or Software Engineers are hired. E4: Those hired at this level should have 3-5 years of industry experience. However, new grads can also be hired at this level, provided they can demonstrate skill and expertise. E5: ML Engineers hired at E5 have at least 5-8 years of industry experience as they are required to lead complex projects on their own. Also considered the “terminal” level before an ML Engineer moves into the management domain as E5 onwards, they perform more managerial responsibilities. E6: Most ML Engineers working at this level have almost 8-15 years of experience. E7: This tier is mostly for ML Directors and Principal ML Engineers with more than 15 years of experience. Based on these levels, the median Facebook Machine Learning Engineer salary range is as follows: Machine Learning Engineer at Facebook Average compensation by level Level name Total Base Stock (/yr) Bonus E3 US$185K US$123K US$40K US$15K E4 US$275K US$166K US$85K US$20K E5 US$411K US$200K US$175K US$30K E6 US$605M US$233K US$310K US$48K E7 US$990K US$278K US$627K US$70K Amazon Machine Learning Engineer Salary Being one of the biggest tech companies in the world, Amazon offers lucrative compensation packages to ML engineers. Amazon has its own Machine Learning Engineer job levels. They are: MLE I: Entry-level ML Engineers with less than 4 years of experience pursuing advanced degrees. They need to be skilled in at least one scripting language and familiar with SQL. MLE II: Mid-level ML Engineers have 4-7 years of experience and may also have the title of ML Engineer II. At this level, ML Engineers usually have a Master’s degree with a good knowledge of coding. MLE III: This level is for ML Engineers who have advanced degrees like Ph. Ds in Machine Learning, Natural Language Processing, etc., based on their area of specialization. The level includes several managerial positions as well. Principal MLE: This level is for ML Engineers with 10+ years of experience. These employees have several management responsibilities and essentially run the teams. Senior Principal MLE: These are highly experienced people who essentially are team heads with multiple teams working with them in a single or even multiple product categories. Based on these levels, the median Amazon Machine Engineer Salary range is as follows: Machine Learning Engineer at Amazon Average compensation by level Level name Total Base Stock (/yr) Bonus MLE I US$180K US$135K US$24K US$20K MLE II US$283K US$160K US$85K US$60K MLE III US$370K US$160K US$170K US$128K Principal MLE US$700K US$160K US$356K US$214K Senior Principal MLE US$900K US$270K US$630K NA Apple Machine Learning Engineer Salary The race to get a Machine Learning job at Apple is quite competitive as the company is renowned for building world-class innovative products. The typical entry-level Apple Machine Learning Engineer’s salary is $180k per year. The company divides the ML Engineer roles into different levels: ICT2: Apple’s entry-level position which usually attracts people with 0-1 year of experience. They need to have at least some knowledge of ML modeling with proficiency in Python. ICT3: People hired at this level should have around 2-5 years of experience with demonstrated knowledge of ML model deployment. Master’s degree holders can usually start out at this level. ICT4: This level is for people with 5-10 years of experience or a Ph.D. in a related field like Computer Science, Machine Learning, etc. Managerial positions also start at this level. ICT5: Senior ML Engineers with 10+ years of experience are hired at this level. They are expected to manage their own teams within the organization or work with cross-functional teams. ICT6: Highly experienced people with experience in managing multiple teams are usually hired at this level. Based on these levels, the median Apple Machine Learning Engineer Salary range is given below: Machine Learning Engineer at Apple Average compensation by level Level name Total Base Stock (/yr) Bonus ICT2 US$180K US$130K US$30K US$20K ICT3 US$240K US$155K US$65K US$20K ICT4 US$345K US$195K US$125K US$23K ICT5 US$472K US$227K US$200K US$50K ICT6 US$990K US$280K US$650K US$60K Netflix Machine Learning Engineer Salary Unlike other companies such as Amazon and Apple, Netflix doesn’t have job levels. The company is known mostly for hiring only senior professionals with at least 4 years of experience. They have also started hiring new graduates for software engineer positions recently. Here are the median salaries of a Software Engineer at Netflix working in the ML/AI domain: Machine Learning Engineer at Netflix Average compensation by level Level name Total Base Stock (/yr) Bonus New Grad Software Engineer US$240K US$180K US$60K $13K Senior Software Engineer US$675K US$645K US$30K $13K Google Machine Learning Engineer Salary At the helm of today’s Machine Learning innovation is Google. So when the company sets out to hire Machine Learning engineers, you know they are looking for only the best of the best. The typical entry-level Google Machine Learning Engineer’s salary is $196K per year. The different job levels at Google: L3 (ML Engineer II): An entry-level position with 0-1 year of experience L4 (ML Engineer III): Requires 2-5 years of experience L5 (Senior ML Engineer): Requires over five years of experience L6 (Staff ML Engineer): Requires 5-8 years of experience L7 (Senior Staff ML Engineer): Requires over 8 years of experience Machine Learning Engineer at Google Average compensation by level Level name Total Base Stock (/yr) Bonus L3 US$196K US$138K US$40K US$21K L4 US$283K US$169K US$85K US$29K L5 US$364K US$190K US$134K US$35K L6 US$535K US$232K US$240K US$53K L7 US$730K US$272K US$375K US$80K Machine Learning Engineer Salaries at Other Tech Companies Knowing the Machine Learning Engineer's salary details for other tier-1 companies can help you evaluate your options better. We’ve curated the salaries associated with each of these companies at different levels: Machine Learning Engineer at Tier-1 Companies Average compensation by level Company Level Name Total Compensation Years of Experience Adobe Software Engineer 1 Software Engineer 2 Software Engineer 3 Software Engineer 4 Software Engineer 5 Software Engineer 5.5 US$200K US$220K US$245K US$324K US$430K US$667K 0-1 1-2 2-5 5-8 8-10 10+ Airbnb L3 L4 L5 US$266K US$295K US$447K 0-1 1-4 4-10 DoorDash E3 E4 E5 US$200K US$330K US$380K 0-2 2-5 5+ IBM Associate Engineer Staff Engineer Advisory Engineer Senior Engineer Senior Technical Staff Member Distinguished Engineer US$100K US$136K US$160K US$232K US$270K US$367K 0-1 1-3 3-8 8-12 12-16 16+ IBM Associate Engineer Staff Engineer Advisory Engineer Senior Engineer sr.Technical Staff Member Distinguished Engineer US$100K US$136K US$160K US$232K US$270K US$367K 0-1 1-3 3-8 8-12 12-16 16+ LinkedIn Software Engineer Senior Software Engineer Staff Software Engineer Senior Staff Software Engineer US$250K US$312K US$522K US$671K 0-3 3-8 8-13 13+ Microsoft 59, 60 61, 62 63, 64, 65 66, 67 68 US$170K US$200K US$320K US$445K US$700K 0-3 3-5 5-8 8-12 12+ Pinterest L3 L4 L5 US$230K US$285K US$465K 0-2 2-3 3-8 Twitter SWE I SWE II Senior SWE Staff SWE Senior Staff SWE US$193K US$255K US$333K US$590K US$600K 0-1 1-3 3-6 6-10 10+ Uber Software Engineer I Software Engineer II Senior Software Engineer Staff Software Engineer Senior Staff Software Engineer US$164K US$260K US$450K US$530K US$800K 0-1 1-3 3-8 8-12 12+ Zillow P2 P3 P4 P5 US$170K US$240K US$350K US$505K 0-1 1-3 3-6 6+ You can learn more about more related topics on our companies page. FAQS ON MACHINE LEARNING INTERVIEW COURSE 1 What are the programming languages used in Machine Learning? Machine Learning modeling is typically done in Python, which has excellent support from inbuilt libraries to do the same. R is another programming language used for experimentation purposes, but it's not as widely used as Python. Some companies might also use MATLAB. 2 Is having a mathematics background a must for ML-related roles? While it is not a must, having familiarity with concepts such as probability, integrals, differentiation, vectors, coordinate geometry, etc., can assist in understanding the idea behind several ML algorithms. 3 Do ML Engineers perform ML modeling/experimentations, or are they just concerned with the deployment part? It depends on the role. Many companies expect MLEs to handle modeling, experimenting, and deployment parts. In contrast, other companies have data scientists to perform ML experiments and MLEs to translate those python ML models to binaries for deployment. 4 Is IK’s Machine Learning Interview Course just for professionals working as ML Engineers in non-FAANG+ companies? No, this course is for everyone – FAANG or non-FAANG. If you have worked as an ML Engineer in any company, or you have relevant background in ML production and design, this course is for you. We will help you in all the preparation that you need for cracking the ML Engineer roles in any company. 5 I am working as a Data Scientist in my current company. Will this course help me transition into an ML Engineer role? That depends. If you have some practical experience in deploying Machine Learning models on a production scale with working knowledge of platforms like AWS, Azure, or GCP, then this course can help you fill in the gaps required for an ML Engineer role. We will cover the relevant Data Structures and Coding, Scalable System Design, and ML System design concepts that you will need to crack the interviews. Additionally, we will also help you modify your resume to highlight your ML Engineer relevant experience to recruiters. 6 Is this Machine Learning Interview course suitable for freshers? No, this course is for working professionals with at least two years of experience working as an ML Engineer or Software Engineer working on ML projects. Additionally, if you are a Data Scientist with practical experience in deploying ML models, you can join the course to transition into the ML Engineer role. 7 Why do we need to learn Scalable System Design concepts for an ML Engineer interview? Scalable system design, specifically ML system design, is an integral part of this role. ML Engineers are required to go through at least 1 or 2 system design rounds. ML Engineers build on the concepts learned from deploying a general software and combine it with the knowledge of ML algorithms to deploy ML models on a production scale. In our course, we cover both scalable system design in 3 weeks and ML system design in every live class of the ML Masterclass. 8 How hard are the coding questions asked in ML Engineer interviews? The coding question difficulty depends on what level and role you are applying for. Typically, entry-level MLE roles would require you to answer medium difficulty questions with some hard problems thrown in. Medium to senior level roles would test you on medium-hard to hard problems. However, if you are applying for an Applied Scientist or Research Scientist position, the coding bar is a lot lower, and you will be asked easy to medium difficulty questions. The coding round can also be skipped if you have 20+ years of experience and for certain Managerial positions. HOW TO ENROLL FOR THE MACHINE LEARNING INTERVIEW COURSE? Learn more about Interview Kickstart and the Machine Learning course by joining the free webinar hosted by Ryan Valles, co-founder of Interview Kickstart. You can also talk to our program advisors to get additional program-related details. Register for webinar It's Free Try 7-Day Email Course for FREE NEXT WEBINAR STARTS IN 00 Day : 13 Hrs : 05 Mins : 15 Secs Already preparing or want a sneak peek? Try the ML Interview Prep 7-day email course DOWNLOAD THE MACHINE LEARNING COURSE BROCHURE Get all the details about the course & pricing. First name *required Last name *required Phone number *required Email address *required By providing your contact information you agree to our Privacy Policy Download Brochure ALMOST THERE... 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