DeepSeek MLE Interview Questions (2026)

Questions reported from DeepSeek machine learning engineer interviews. This bank tracks 10 questions asked at DeepSeek (4 with free model answers). Practice them live with DeepOffer's AI voice interviewer.

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#45 Explain multi-head latent attention and the motivation DeepSeek had for introducing it. ★
#51 Describe mixture-of-experts and how it grows model capacity without growing per-token compute. ★
#65 Walk through DeepSeekMoE and how it differs from a conventional top-2 MoE such as Mixtral.
#66 Explain DeepSeek-V3's auxiliary-loss-free load balancing: what was wrong with the auxiliary loss and how the bias trick works.
#67 Describe multi-token prediction and the reason for training with it.
#85 Explain GRPO and why dropping the value network matters at scale. ★
#95 R1-Zero was trained with RL and essentially no SFT — what did that demonstrate, and why did full R1 bring SFT back?
#96 Explain what actually breaks in FP8 training at 671B scale and how you make it stable.
#192 Sketch how you would serve a 671B-parameter MoE with low latency under GPU-memory constraints. ★
#301 Implement top-k MoE routing with a shared expert in PyTorch and point out the efficiency and correctness traps.