The opportunity
Anthropic's inference fleet serves Claude to millions of users across our own products and the world's largest cloud platforms. The stack that makes this possible is deep and tightly coupled: accelerator kernels, model servers, distributed routing, autoscaling, capacity management.
What you'll do
Run cross-layer performance investigations across throughput, latency, and: reliability, sizing the gap between actual fleet performance and theoretical rooflines, identifying root causes, and quantifying the value of closing them
Own and improve the correctness evaluation pipeline that validates model: output quality across hardware platforms, numerics, and serving configurations, and lead the investigation when it catches a regression
Build the observability, dashboards, and modeling tools that make throughput,: latency, cost, reliability, correctness, and their interactions legible across the stack
Partner with kernel, serving, routing, autoscaling, and capacity teams to: prioritize and land the highest-impact optimizations your analysis surfaces
Ruthlessly stack-rank a large surface area of opportunities by impact and: effort, and say no to the ones that don't make the cut
Hands-on performance engineering experience: profiling, roofline analysis, latency/throughput optimization, and root-cause investigation in complex production systems
What they're looking for
- Experience with ML systems, especially training or inference infrastructure: or general LLM serving stacks. Direct large-scale inference experience is a strong plus
- Familiarity with GPU/TPU/accelerator performance concepts (memory bandwidth,: kernel overheads, quantization, collective communication). Reasoning about these matters more than having written kernels yourself
- Experience with reliability engineering for high-throughput services: autoscaling, load balancing, request routing, tail latency
- Experience with model evaluation or numerical regression-detection pipelines
- Experience building observability or telemetry for distributed systems
- Comfortable having impact through influence and evidence rather than direct ownership
