Anthropic is looking for a Staff Engineer to lead the technical direction of the Inference Runtime, a shared, accelerator‑agnostic core of the inference serving stack. This senior IC role involves owning the architecture, roadmap, and build structure of the runtime, working closely with engineering managers, infrastructure teams, and platform engineers. Responsibilities include performance‑sensitive work in Rust and Python, optimizing accelerator usage across GPUs, TPUs, and Trainium, building robust validation mechanisms, and mentoring team members. The role requires deep experience in systems engineering, ML infrastructure, and accelerator ecosystems, as well as a proven track record of driving metrics‑based improvement and technical alignment across teams.
Staff+ Software Engineer, Inference Runtime at Anthropic
Remote - Remote (Travel-Required), San Francisco, CA, Seattle, WA, New York City, NY, United States
More jobs at AnthropicSalary
USD 405,000 - 485,000
Requirements
Skills
- Deep background in systems engineering or ML infrastructure with hands‑on performance profiling, latency and throughput optimization, and systems debugging at scale
- Real depth in at least one accelerator ecosystem (CUDA/GPU, TPU, or Trainium/AWS Neuron) and genuine appetite to keep the runtime agnostic across all of them
- Significant software engineering experience in high‑performance, large‑scale distributed systems serving millions of users
- Track record of defining and using engineering metrics to drive improvement (e.g., setting SLOs, reducing escape rates, improving release times, latency, throughput)
- Experience driving technical alignment across organizational boundaries and advocating for team needs while contributing to shared infrastructure
- Strong written and verbal communication skills with the ability to influence technical direction without formal authority
- 8+ years of software engineering experience with significant time as a technical lead or anchor on a platform, inference runtime, or ML infrastructure team
- Experience with ML compiler toolchains (XLA, Triton, NeuronX) or accelerator driver/firmware management at scale
- Background operating a production validation surface at scale (shadow traffic, canary populations, automated baseline comparison, fast rollback)
- Experience with deterministic or simulation‑based testing for hardware‑dependent systems
- Experience with CI/CD systems at scale, particularly for workloads involving accelerator hardware
- Familiarity with Kubernetes‑based development and job scheduling environments
- Prior tech lead experience on a developer productivity or platform engineering team at a fast‑growing AI/ML company
Responsibilities
- Set technical direction for the team, owning the architecture and roadmap for the shared runtime of the inference serving stack
- Own and evolve the accelerator‑agnostic runtime itself – its interfaces, internal boundaries, and build structure – including hands‑on work in a performance‑sensitive Rust and Python codebase
- Keep the platform’s expansion cost low by ensuring new models and deployment targets pay only for their own specialization, and edge cases stitch back into the core easily
- Drive efficient accelerator usage – utilization, scheduling, memory management – across GPU, TPU, and Trainium
- Build the runtime’s validation surface around partitioned builds, change‑scoped testing, and canary/shadow/rollback as first‑class mechanisms
- Act as a technical counterpart to Anthropic’s central Infrastructure org on the compilers, build systems, and toolchains the runtime depends on, contributing Inference’s performance and correctness requirements, and making the call on build vs. adopt
- Mentor engineers on the team through design review, code review, and direct collaboration, raising the technical bar without owning headcount
Technologies
RustPythonGPUTPUTrainiumCUDAXLATritonNeuronXKubernetesCI/CD
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