ML engineer interview prep, head-to-head
Generic copilots cover ML at the textbook level. MLE rounds need ranking-system trade-offs, KV-cache reasoning, online-vs-offline metric reconciliation — the stuff that actually breaks in production.
Built for the senior MLE loop
ML system design — six scaffolds
Recommender, ranking, search, fraud, ads, ML platform. Each has its own scaffold: candidate generation strategy, scoring stage, re-ranking, feature pipeline, training cadence, online evaluation. The copilot picks the right scaffold the moment the interviewer states the prompt.
Training-infra rounds
Distributed training questions (data-parallel vs model-parallel vs pipeline-parallel, gradient accumulation, gradient checkpointing, mixed precision, ZeRO partitioning) get scaffolded with the actual trade-off — memory pressure vs throughput vs convergence — that a senior MLE is expected to articulate.
Inference at scale
KV-cache management, request batching, speculative decoding, quantisation (int8 / int4 / FP8 trade-offs), TTFT vs total latency, GPU utilisation. The copilot maps the question to the canonical bottleneck and walks the mitigation tree.
Offline ↔ online metric reconciliation
Most ML loops include the trap question: "your offline NDCG improved by 3%, online CTR dropped — why?" The copilot walks the canonical causes (training-serving skew, feature lag, position bias, novelty, distribution shift) and the diagnostic order to investigate them.
Feature stores + leakage prevention
Feature engineering rounds are easy to ace and easy to fail. The copilot scaffolds the leakage taxonomy (label leakage, time leakage, train/serve skew), the feature-store decision (offline vs online vs unified), and the point-in-time correctness guarantee — all in interview-length answers.
Coding rounds — PyTorch / TensorFlow internals
Implement a custom loss. Write a forward pass with attention. Manually compute the gradient. These rounds reward fluency with autograd semantics, eager vs graph mode, and the difference between nn.Module and a functional pass. The copilot keeps you on the canonical path.
