Apple·Machine Learning Engineer·Technical Phone Screen
Apr 2026
Apple ML engineer interview focused entirely on LLM post-training. One long open-ended technical discussion covering data pipelines, loss objectives, and evaluation strategies. Dense but fair if you actually know the material.
- Walk me through how you'd collect and curate SFT data, including filtering and deduplication.
- How is preference pair data generated for DPO, and what are the tradeoffs between human ranking versus using a model as a judge?
- Explain the SFT training objective. Why do you mask the prompt tokens when computing the loss?
- Break down the DPO loss function. What is the role of the beta parameter and why does DPO not need a separate reward model or PPO?
- Why is a reference policy needed in DPO, and what does the KL constraint actually enforce?
- What evaluation methods would you use after post-training, and how would you detect overfitting, mode collapse, or reward hacking?
- Compare SFT, RLHF, and DPO. When would you choose one over the others?
“I started with human-written demos and talked through quality filtering, but I fumbled when they pushed on deduplication strategy.”