About this role
About Anthropic
Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.
About the role
Anthropic's Inference organization serves Claude to millions of users and enterprise customers with the speed, reliability, and efficiency that frontier AI demands. We build across GPUs, TPUs, and Trainium, and the complexity of our development environment grows with every platform we add. We're looking for a Staff engineer to be the technical lead for Inference Developer Productivity: the team that makes every engineer in the org dramatically more effective at building, testing, and shipping inference software.
This is a senior IC role with broad technical ownership. You'll set technical direction for the team's toolchains, workflows, and feedback loops, and you'll be the one making the hard calls on architecture, prioritization, and tradeoffs across heterogeneous accelerator platforms. You'll pair with the team's Engineering Manager, who owns hiring and people development, while you own the technical roadmap and drive the work. You'll also partner closely with Anthropic's central Infrastructure org, where company-wide developer productivity lives, to make sure Inference's multi-accelerator reality is well served without duplicating effort.
This role is for someone who has been the technical anchor on a platform or infrastructure team before, who thinks in systems and feedback loops, and who gets real satisfaction from the moment another engineer stops fighting their environment and starts shipping.
Key responsibilities
• Set technical direction for Inference Developer Productivity, owning the architecture and roadmap for toolchains, dev environments, and CI/CD across GPU (CUDA), TPU, and Trainium platforms
• Be the technical owner of accelerator toolchain management: compilers, drivers, libraries, frameworks, kept current, compatible, and well-tested so Inference engineers focus on model serving instead of environment archaeology
• Design and build infrastructure for efficient accelerator usage during development, including devbox environments, pre- and post-land validation automation, and shared tooling that reduces the cost of working across heterogeneous hardware
• Define and instrument productivity metrics for the Inference org, building the dashboards and alerting that surface regressions early (smoke tests red for extended periods, build times creeping up, toolchain breakages) and drive them to resolution
• Proactively hunt down bottlenecks, toil, and friction across Inference engineering workflows, then design and build the systems that eliminate them
• Act as the technical counterpart to Anthropic's central Infrastructure org, aligning on shared developer productivity initiativ