Anthropic
Performance Engineer, Inference Systems
About this role
Anthropic seeks a Performance Engineer to optimize and validate their large-scale Claude inference system across throughput, latency, reliability, and correctness. You'll investigate performance gaps across the entire stack, own correctness evaluation pipelines, and partner with infrastructure teams to drive high-impact optimizations.
What you'll do
- Conduct cross-layer performance investigations to identify gaps between actual fleet performance and theoretical limits
- Own and improve the correctness evaluation pipeline that validates model outputs across hardware platforms and configurations
- Build observability tools, dashboards, and models to make performance metrics and their interactions visible across the stack
- Partner with kernel, serving, and infrastructure teams to prioritize and implement optimization opportunities
- Investigate performance regressions and root causes in production inference systems
- Stack-rank optimization opportunities by impact and effort to guide team priorities
What they're looking for
- Performance engineering and profiling in production systems
- Python and ability to work with large codebases
- Data analysis tools such as SQL and pandas
- Roofline analysis and latency/throughput optimization
- ML inference systems and LLM serving infrastructure
- GPU/TPU accelerator performance concepts
- Distributed systems reliability engineering
- Technical communication and influence through evidence
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Anthropic
Anthropic builds Claude, an AI assistant, and is hiring for engineering roles across infrastructure, data systems, and security that support both AI research operations and the company's internal technology needs. The company seeks infrastructure engineers, systems integrators, data scientists, and security specialists to build production-scale systems for training data pipelines, financial operations, developer productivity measurement, research infrastructure, and server firmware security.
- Website
- anthropic.com
Likely interview questions
- Walk us through a time you profiled a complex production system to identify a performance bottleneck. How did you isolate the root cause across multiple layers?
- Describe your experience with roofline analysis or similar theoretical performance modeling. How have you used it to identify gaps between actual and peak system performance?