Meta
ProML Engineer interview prep
Based on 6 reports, the Meta MLE interview process is heavily weighted toward general software engineering fundamentals, with standard coding and system design rounds appearing more frequently than specialized ML coding. Candidates across mid to staff levels should expect coding assessments focused on medium to hard algorithmic challenges involving complex string manipulation, sliding window techniques, and the design of efficient data structures. The evaluation is rounded out by system design and domain knowledge assessments, reflecting a loop that is deeply inclusive of core software engineering competencies.
Based on 6 interview reports · 73 questions
- ·Design a multi-task ranking model for a marketplace where you're jointly optimizing click-through, conversion, and long-
- ·Design an embedding-based retrieval system for a marketplace with 500k items, where 1000 new items are added daily. Many
- ·Design an online experimentation platform for a ranking system serving 10M daily users. You need to run 20+ concurrent A
- ·Explain multi-task learning architectures for ranking: shared-bottom, MMoE, PLE, and AITM. For each, derive the gradient
- ·Walk through two-tower retrieval end-to-end. Derive the training objective. What information leakage problems arise when
- ·Explain position bias in ranking from first principles. Derive the IPW estimator and its variance problem. Compare resul
- ·You're the most senior ML engineer on a team of 6. Two mid-level engineers keep proposing overly complex model architect
- ·You've been asked to lead the technical strategy discussion for your team's next-year ML roadmap. Product wants 5 new fe
- ·You need to convince a product manager and a VP that an ML system is NOT worth building — a simpler heuristic would achi
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