Scale AI MLE Interview Questions (2026)

Questions reported from Scale AI machine learning engineer interviews. This bank tracks 13 questions asked at Scale AI (4 with free model answers). Practice them live with DeepOffer's AI voice interviewer.

← All 465 questions

Practice this question with an AI interviewer

Get asked follow-ups live, then receive a scored report — like a real MLE interview loop.

Start AI mock interview

#52 Distinguish pretraining, supervised fine-tuning, and preference optimization. ★
#79 Argue whether encoder-decoder models are still needed when decoder-only models can do everything.
#84 Explain DPO, why it displaced PPO-based RLHF at many labs, and when online RL is still preferable. ★
#92 Explain RLVR and where it beats a learned reward model. ★
#93 Explain reward hacking in RLHF and how labs address it.
#98 Compare SFT, RLHF, DPO, and RLVR for improving an instruction-tuned model: what data each needs and when to pick which.
#221 Design an enterprise RAG assistant over 10M documents with per-user permissions. ★
#249 An eval pipeline reports a 6-point drop for a customer's model between Tuesday and Wednesday with no model change — debug it.
#281 Design an RLHF preference-data pipeline producing 100k prompt-response comparisons a week with quality guarantees.
#282 With no ground truth on subjective preference judgments, explain how to measure and improve label quality.
#283 Design an RL environment for booking a multi-city trip in a web travel app: specify the reward and stop the policy from hacking it.
#336 Build the task-lifecycle core of an annotation platform, then add k-annotator consensus, priority re-review, and annotator cooldowns.
#337 Given annotation sessions as (start, end) timestamps, return the peak number of concurrent annotators and the intervals at peak load.