Explore Chip Huyen's insights on machine learning interviews in this 44-minute talk from Full Stack Deep Learning's November 2019 conference. Gain valuable perspectives on various ML roles, including applied research, ML engineering, and data science positions. Discover the six common career paths in the field and learn whether a PhD is necessary for certain positions. Examine the challenges companies face in hiring ML professionals and understand why many struggle to find the right candidates. Analyze the interview process, including examples of good and bad interview questions, alternative interview formats, and the importance of referrals. Learn how candidate experience affects offer acceptance rates and gather general tips to improve your chances of success in ML interviews.
Chip Huyen on Machine Learning Interviews - November 2019
Overview
Syllabus
Intro
Applied research
Research scientist Research engineer
ML Engineers
Data scientist
Big companies
Six common paths
Do you need a PhD?
The only role that might require a PhD is (applied) research scientist
Companies hate hiring
Companies don't want the best people
Companies don't know what they're hiring for
Most recruiters can't evaluate technical skills
Most interviewers are bad
Interview outcome depends on many random variables
Interview process
Bad interview questions
Examples of good interview questions
Alternative interview formats
The higher the onsite-to-offer ratio, the more likely offers are accepted
How important are referrals?
Candidates with negative experience are less likely to accept offers
General tips
Taught by
The Full Stack
