How to Interview and Hire ML/AI Engineers

Hiring well is the highest leverage activity we can do for the mission and organization. And running effective interviews is key to hiring well. We can think of the interview process as a system: Given a candidate, assesses whether they are a good fit for the role and team. Thus, to hire well, the interview system should be reliable and valid, with minimal noise.

In this write-up, Jason and I will share a few things we’ve learned about interviewing candidates for machine learning (ML) and AI roles. First, we’ll discuss what technical and non-technical qualities to assess. Then, we’ll share how to calibrate phone screens, and run the interview loop and debrief. Finally, we’ll wrap up with some tips for interviewers and hiring managers, as well as our opinionated take on some traits of a good hire. (And like all our writing online, opinions our own.)

For most ML/AI roles, a basic proficiency in software engineering is expected. Depending on the role, we’ve seen simple 30-60 minute coding exercises that ask a candidate to:

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