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Interview prep for AI Compute Infra (GPU / Inference) roles. Senior-level interview prep for a GPU and accelerator infrastructure role — own clusters, schedulers, vLLM / Triton / TensorRT, observability for GPU utilization and cost, and the production substrate that lets the rest of the org train, evaluate, and serve models in-house.
The role, in plain English
This guide is for engineers and product folks preparing for the role described in the JD (one of many similar roles across fintechs and crypto exchanges). The work is recognizable across the segment — at Coinbase, Gemini, Stripe, Robinhood, and similar.
"Own and operate GPU and accelerator clusters used for training, inference, evaluation, and experimentation, including drivers, runtimes, kernels, device plugins, node configuration, scheduling primitives, and workload isolation."
Despite different titles, the work has the same shape: GPU clusters, ML serving, Kubernetes, capacity planning, with the technical and judgmental constraints that imposes.
What the rounds typically test
Loops for these roles usually mix five round types — each gets dedicated chapters below:
The folder, in reading order
The numbering follows the order you should read in. Five sections:
Section A — Orient (read first)
| File | Why |
|---|---|
| 01-the-role | Decode the role and the stack |
| 02-positioning-from-scratch | Mindset before content — how to interview honestly when light on direct production experience |
Section B — Technical core
| File | Why |
|---|---|
| 03-core-fundamentals | Foundational concepts the rest builds on |
| 04-deep-dive-primary | The single most important technical area |
| 05-deep-dive-secondary | The second pillar that interviewers will probe |
| 06-applied-patterns | Production patterns and how they show up |
| 07-evaluation-quality | Correctness, reliability, observability |
| 08-error-handling | Failure modes and recovery |
| 09-governance-and-audit | Risk tiering, audit trails, HITL |
Section C — Coding
| File | Why |
|---|---|
| 10-coding-fundamentals | DSA patterns and a working language |
| 11-coding-problems | Hand-picked problems with drill mode |
Section D — Production
| File | Why |
|---|---|
| 12-data-pipelines | Data flows, integrations, batch vs streaming |
| 13-deployment-and-ops | CI/CD, environments, rollback, observability |
Section E — Reference + execution
| File | Why |
|---|---|
| 14-domain-context | GPUs, CUDA, NCCL, vLLM, Triton, TensorRT, KServe, Ray, K8s, NVLink vocabulary |
| 15-interview-questions | Practice Q&A — drill these out loud |
| 16-day-of | Tactics, traps, what to ask them. Reread morning of |
Suggested study schedule
If you have 7+ days
- Day 1:
01,02(orient) →03(fundamentals) - Day 2:
04,05(deep dives) - Day 3:
06,07(patterns + evals) - Day 4:
08,09(error handling + governance) - Day 5:
10,11(coding on a timer) - Day 6:
12,13(data + deployment) - Day 7: Drill
15. Read14and16. Sleep.
If you have 2-3 days
01, 02, 03, 04, 05, 07, 08, 11, 15, 16. Skim the rest.
If you have < 24 hours
01, 02, 11 (the named problems), 15, 16. Skim 04, 05, 07 headings only.
Two practical things to do before interview day
Reading is cheaper than building, but building sticks. If you can find an evening or two, the per-guide chapter 06-applied-patterns calls out specific things you can prototype in 30-90 minutes. Doing one of them closes more of your gap than the same time spent rereading.
The single most important reframe
Many candidates feel underqualified going into senior interviews — the field moves fast, the vocabulary is dense, and "real" production experience is unevenly distributed. Two things matter:
- You're learning the precise vocabulary practitioners use. This folder fixes that.
- You're being honest about your gaps, not bluffing. That posture, done right, is more persuasive than fake seniority. Read 02-positioning-from-scratch first; the entire interview goes better when your inner posture is "honest, prepared, fast learner."
Say so cleanly: "I haven't worked with X. My closest reference point is Y. Want me to reason about X from first principles?"
Interviewers respect that far more than bluffing.
What "winning" looks like in these rounds
- Vocabulary fluency — using the right terms in the right places.
- Sound reasoning — given a novel problem, you arrive at a defensible architecture by thinking, not by recall.
- Failure-mode instinct — you reach for "what could go wrong" before "what's cool."
- Domain-aware judgment — your defaults respect the constraints of the domain.
- Honesty at the edge of what you know — and graceful redirection.
- Live learning — when they teach you a concept mid-interview, you visibly absorb and use it later.
You're closer than you think. Let's go.