Apple MLE Interview Questions (2026)

Questions reported from Apple machine learning engineer interviews. This bank tracks 21 questions asked at Apple (11 with free model answers). Practice them live with DeepOffer's AI voice interviewer.

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#2 How Can Softmax Be Calculated Without Numerical Overflow? ★
#6 How Do L1 and L2 Penalties Shape a Model Differently? ★
#28 Explain a Transformer from Input Embeddings Through Its Output Head
#35 Given Item Counts, How Would You Return the K Most Frequent Values? ★
#36 How Would You Build an O(1) Least-Recently-Used Cache? ★
#43 Describe the KV cache, derive its memory formula, and discuss what it costs at production scale. ★
#53 Compare greedy, beam, top-k, top-p, and temperature decoding, and identify when each one fails. ★
#88 Compare LoRA, prefix tuning, prompt tuning, and full fine-tuning, and state when each is the right choice. ★
#170 Compare FP16, BF16, FP8, INT8, INT4, and FP4 for serving and name what breaks at each step down. ★
#173 Define TTFT, TPOT, ITL, and throughput, and explain how they trade against each other. ★
#198 Design a routing layer that decides whether a request runs on-device, on a first-party server model, or on a third-party model.
#199 Explain how you would run a ~3B model on a phone with tight memory and power budgets versus in a datacenter.
#200 Contrast post-training quantization with quantization-aware training, and explain what breaks at 2–4 bits and how to recover quality.
#201 Walk through diagnosing and fixing an on-device feature whose time-to-first-token is 1.8s.
#296 Implement a correct multi-head self-attention forward pass with projections, reshaping, and masking. ★
#300 Implement top-k, top-p, and temperature sampling over a logits vector. ★
#378 Describe a time you had to make progress with incomplete information about what you were building.
#382 Present your strongest past project for 30 minutes and take questions for 15.
#419 Design a hospital voice assistant: real-time STT+LLM+TTS pipeline, HIPAA compliance, and medical latency budgets
#431 Design over-the-air model-update distribution across millions of on-device models: delta updates, staged rollout, and rollback
#451 Debug a U-Net segmentation model live in PyTorch/NumPy and find the bugs