optimization

Truncate? Summarize? Rerank? Map-reduce? A production problem every RAG deployment hits, with a menu of fixes interviewers expect you to weigh, plus the diagnostic question to ask first.

TTFT vs tokens-per-second, the output-length lever everyone forgets, and why streaming is the highest-ROI fix that changes no latency at all. The five-lever answer with real numbers.

The KV-cache mechanics behind the discount, the prefix rule that silently breaks caching for most teams, and the workloads where caching cuts bills 50-90% versus the ones where it does nothing.

An ML coding screen that doubles as a calculus check. Interviewers watch whether you can write the MSE gradient without looking it up, explain what the learning rate actually does, and say out loud when you'd just solve the normal equation instead.

A two-minute explanation, then the real test: learning-rate pathologies, why we use mini-batches, and the one practical diagnosis, reading a loss curve, that customer-facing engineers actually need.

Most candidates recite the Adam update equation and stop. The interviewer wants the decision: when plain SGD with momentum still wins, why Adam is the safe default for new architectures, and how you diagnose a training run that is silently dying from a gradient that shrank to zero.

Gradient descent is walking downhill on the loss surface, one step proportional to the slope. Knowing when to stop is the part that separates people who have trained models from people who have read about them: it is the validation curve, not the training curve, that tells you.

The small-files problem is the #1 self-inflicted lakehouse wound FDEs find in the field. The causes are always the same three things, and the fix has a prevention half most candidates forget.

Looks like an algorithms question; it's actually an operations question wearing one. The candidates who fail reach for 'solve VRP'; the ones who pass design for the 2pm reality that invalidates every morning plan.

A live-reported Palantir FDSE decomp with a built-in twist: after you design for the driver, the interviewer flips you to Uber's side. The expected-value-per-hour model, the tip-prediction ethics trap, and how to survive the flip.

A port is three coupled scheduling problems in a single prompt: where ships dock, which cranes work them, and where containers land in the yard. The strong answer decomposes them, names where they couple, and refuses to solve the whole thing as one giant optimizer.

The two access-pattern killers behind most 'my kernel is 10x slower than cuBLAS' mysteries. The matrix transpose walks straight into both, which is why NVIDIA loves asking about it.