How to Interview and Hire ML/AI Engineers
Eugene Yan
The article discusses frameworks for interviewing and hiring machine learning and AI engineers, covering technical skills like software engineering proficiency, data literacy, understanding ML model opacity, and evaluation methodologies. Key technical assessment areas include coding exercises (30-60 minutes), data analysis skills, comfort with model uncertainty, and knowledge of evaluation practices, with specific follow-up questions provided for each. For more research-focused roles, additional assessment includes science breadth (familiarity with ML domains like recommender systems and language modeling), science depth (rigor on past projects), and science application (solving practical problems relevant to the team), alongside non-technical dimensions like ambiguity tolerance, influence, complexity, and execution.
Why it matters
What to interview for, how to structure the phone screen, interview loop, and debrief, and a few tips.
