Anthropic·Machine Learning Engineer·Onsite - System Design / Architecture
Apr 2026
Anthropic ML engineer interview focused entirely on production systems, not modeling. The whole thing was about what happens after you ship: infra, observability, performance tuning. Pretty intense if you've mostly lived in notebooks.
- How would you scale the infrastructure for a production ML serving system, covering things like autoscaling, GPU vs CPU placement, multi-region deployment, and capacity planning?
- What metrics and SLOs would you define for an ML production system, and how would you set up alerting and drift detection?
- Walk through your approach to optimizing ML inference performance, including batching strategies, caching, quantization or distillation tradeoffs, and request shedding under load.
- Walk through how you'd diagnose a p99 latency spike or an accuracy regression in a live ML system, end to end.
“I spent too long on the model side before realizing they explicitly didn't care about that.” The rest of the author's notes on Machine Learning Engineer interview at Anthropic, Onsite - System Design / Architecture round, covers how they worked through the question, what the panel pushed back on, and what they would do differently.
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