Inference / AI platform engineer¶
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AI Engineering / AI Leadership · Roles covered: Inference and performance engineer, AI platform / MLOps engineer, AI platform lead
Makes AI computation and shared services perform: bottlenecks, validated optimisations and telemetry.
On this page: What the role involves · How it is interviewed (4) · Questions (19) · Preparation
What the role involves¶
This family makes AI computation and shared services usable under performance and operating constraints. Anthropic's Performance Engineer, Inference Systems investigates across serving layers, measures throughput and latency, and checks output correctness across hardware and configurations. It explicitly works with component owners rather than owning every component. OpenAI's agent-platform example adds shared execution and rollout infrastructure.
Sources: Performance Engineer, Inference Systems, Anthropic, retrieved 2026-09-29 · Software Engineer, Codex Core Agents, OpenAI, retrieved 2026-09-29
Deliverables and skills: a diagnosed bottleneck, validated optimization, useful telemetry and reliable shared interfaces. Profiling, numerical reasoning and distributed-systems knowledge matter; writing accelerator kernels is one specialism, not a requirement to infer from every platform title. Distinguish model-serving performance from application orchestration when reading the remit.
How it is interviewed¶
Stages that sources report for these roles, taken from the company pages. Markers show the basis; interview loops change, so check the dates.
| Company | Stage | What happens | Basis |
|---|---|---|---|
| Anthropic | Recruiter screen AI platform / MLOps engineer |
15–30 minutes on background and motivation, including why Anthropic and interest in AI safety. The recruiter warned that candidates can pass technically and still fail on values. | 🗣 candidate report Anthropic Staff Software Engineer, Infrastructure Interview Experience, Aced (formerly Exponent), candidate report, retrieved 2026-09-26 |
| Anthropic | Onsite AI platform / MLOps engineer |
One candidate had five rounds: system design (distributing a large file to thousands of machines), a practical coding project (an image-processing pipeline), a project deep dive on cross-team agreement and conflict, a behavioral round and a culture round. | 🗣 candidate report Anthropic Staff Software Engineer, Infrastructure Interview Experience, Aced (formerly Exponent), candidate report, retrieved 2026-09-26 |
| Anthropic | Values and culture round AI platform / MLOps engineer |
Questions about a time the work conflicted with your values and how you weigh delivery pressure against safety or security concerns. | 🗣 candidate report Anthropic Staff Software Engineer, Infrastructure Interview Experience, Aced (formerly Exponent), candidate report, retrieved 2026-09-26 |
| Anthropic | Original performance engineering take-home (retired) Inference and performance engineer |
Introduced in early 2024, the original task asked candidates to optimise a serial program for a simulated accelerator using multicore, vector and instruction-level parallelism, with AI assistance allowed. The January 2026 account says this task was retired: a second version removed multicore, and the replacement used puzzles with a small instruction set. | ✅ confirmed by the company Designing AI resistant technical evaluations, Anthropic, published 2026-01-21, retrieved 2026-09-29 |
Company pages that cover these roles: Anthropic.
Questions¶
Reported for these roles¶
Questions whose source says they were asked for one of these roles.
- In Anthropic's original performance-engineering take-home, introduced in 2024 and subsequently retired, optimise a serial program for a simulated accelerator using multicore, vector and instruction-level parallelism.
- Coding · Inference, serving and cost · Asked at: Anthropic ✅
- Sources (Inference and performance engineer): ✅ Designing AI resistant technical evaluations, Anthropic, published 2026-01-21, retrieved 2026-09-29
Also practise¶
An editorial selection: relevant to the role, but not reported for it.
- Contrast prefill with autoregressive decode and explain when compute or memory bandwidth becomes the bottleneck.
- Knowledge · Inference, serving and cost · ✍ Answer
- Explain iteration-level batching and how requests enter and leave a running inference batch.
- Knowledge · Inference, serving and cost · ✍ Answer
- Estimate a serving memory budget for a 70-billion-parameter model, explicitly choosing precision, context and concurrency assumptions.
- Applied scenario · Inference, serving and cost · ✍ Answer
- Distinguish time to first token, time per output token, inter-token latency and throughput when comparing serving systems.
- Knowledge · Inference, serving and cost · ✍ Answer
- Build a roofline estimate for single-request decoding of a 70B model on an H100; check first whether the chosen representation fits.
- Applied scenario · Inference, serving and cost
- How would you benchmark vLLM, SGLang, TensorRT-LLM and a custom runtime for a specific workload?
- Applied scenario · Inference, serving and cost
- You are asked to reduce serving cost by an order of magnitude. Rank the levers and explain how you would test whether that target is achievable.
- Applied scenario · Inference, serving and cost · ✍ Answer
- After a deployment, p99 latency doubles although model weights are unchanged. How do you isolate the cause?
- Applied scenario · Inference, serving and cost · ✍ Answer
- Before launching an assistant that can use tools, how would you organise adversarial testing, and what would a clean result let you claim?
- System design · Safety, security and governance · 🧪 generated from job-posting themes · ✍ Answer
- Design an LLM gateway with provider routing, failover, caching, rate limits and enforceable spending budgets.
- System design · AI system design · ✍ Answer
- Define a strategy for an internal AI platform, including who owns its success metrics.
- Product case · Senior · AI platform and operating model · ✍ Answer
- How would you support multiple model providers when some deployment environments permit only a subset of models?
- System design · Senior · AI platform and operating model · ✍ Answer
- As the investment owner, decide whether to renew a managed AI service or fund an internal replacement. The vendor’s price doubles in six months and exported data alone cannot reproduce its behaviour. What decision and exit plan would you approve?
- Applied scenario · AI platform and operating model · 🧪 generated from job-posting themes · ✍ Answer
- Several product teams share one internal AI gateway. How would you keep one team's traffic, data and caches from affecting another's?
- System design · AI platform and operating model · 🧪 generated from job-posting themes · ✍ Answer
- A provider announces that a model version your products depend on retires in three months. How would you run the migration across the teams that use it?
- Applied scenario · AI platform and operating model · 🧪 generated from job-posting themes · ✍ Answer
- Inference spend on a shared AI platform doubles in a quarter and no team recognises the bill as theirs. How would you make the cost attributable and controllable?
- Applied scenario · AI platform and operating model · 🧪 generated from job-posting themes · ✍ Answer
- Your AI platform's latency and availability depend on an external model provider. What would you promise internal teams, and how would you keep that promise?
- System design · AI platform and operating model · 🧪 generated from job-posting themes · ✍ Answer
- During an AI rollout, a provider changes model behaviour and harmful actions reach customers. The previous model cannot be restored immediately. As the programme lead, how do you coordinate containment, customer response and a defensible restart?
- Applied scenario · Programmes and delivery · 🧪 generated from job-posting themes · ✍ Answer
Preparation¶
Preparation route: engineering baseline with extra inference depth, latency metrics and runtime comparison. Produce a load-test report that fixes workload and quality requirements, separates queueing from execution, and tests a tail-latency regression. A platform-lead variant also needs operating ownership; a cost worksheet alone does not prove serving performance.
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