Meta AI Platform & Model PM interview questions

Quantization offers large savings with small average quality loss, and the averages hide where the loss lands. The interviewer wants evaluation on real workloads, by segment, before any cost win is claimed.

A vocabulary question that unlocks cost, latency and on-device decisions. The interviewer wants the idea, the trade-off and when it matters to a product.

A serving optimisation whose value depends on traffic shape. The interviewer wants the mechanism, the evidence that gains shrink under load, and a decision tied to the product's latency and throughput needs.

A platform question at principal level. Every team running its own training and serving stack wastes GPUs and creates risk; a central platform that is hard to use gets bypassed. The design has to make the shared path the fastest one.

Model documentation is read by developers deciding whether to build on a model, by enterprises doing due diligence, and by regulators. The interviewer wants contents that serve all three, honestly.

A resource allocation question with no painless answer. The interviewer wants a transparent framework, efficiency gains before rationing, and decisions leadership can stand behind.

English-only evaluation hides regressions that land on most of the world's users. The interviewer wants a plan that covers languages by importance, uses native evaluation, and gates release on per-language results.

A fundamentals question for platform and model PM roles. The interviewer wants the stages in order and, at each, the decision a PM is responsible for.

Budget for the whole labeling job, distinguish disagreement from poor work and test whether the resulting data improves the model.

Synthetic data is cheap and fast, but provider terms, inherited errors and narrow coverage decide whether it helps. Check the contract first, then design the data and the evaluation.

A platform metrics question. Token volume is the obvious answer and the incomplete one. The interviewer wants the developer funnel, reliability, economics and migration health in one coherent set.

A role-understanding question common in lab interviews. A crisp answer shows you know who the customers are, what decisions you own, and how success is measured.

A launch question where positioning, pricing, evaluation and capacity all have to line up. The interviewer wants to see you avoid cannibalising the wrong tier and give developers what they need to adopt it.

Deprecations are where platforms earn or lose developer trust. The interviewer wants a timeline, a recommended replacement with evidence, migration tooling, and special handling for customers who cannot move easily.

The spreadsheet comparison everyone gets wrong by assuming full GPU utilisation and forgetting the team. The interviewer wants a break-even with honest utilisation, operating costs and a quality check.

Rate limits protect capacity and fairness, and they shape the developer experience as much as any feature on the API. The interviewer wants limits by dimension, tiers that grow with trust, and clear behaviour when limits hit.