Staff+ Software Engineer, Inference Velocity

Anthropic · Remote-Friendly (Travel-Required) | San Francisco, CA | Seattle, WA | New York City, NY · Software Engineering - Infrastructure · listed June 12, 2026

The shape of it

Seniority
Staff
Experience asked
8+ years
Where
Hybrid
Stated pay
$405,000 – $485,000 USD
Requirements listed
7
Length
1,244 words

In the posting’s own words

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.

What it asks for · 7

  • 8+ years of software engineering experience, with significant time as the technical lead or anchor on an infrastructure, platform, or developer productivity team
  • Deep background in systems engineering, build/test infrastructure, or ML infrastructure, with the ability to go hands-on with toolchain issues, CI/CD pipelines, and developer workflow optimization
  • Experience owning toolchains or development environments for compute-intensive workloads (ML training or inference, HPC, large-scale distributed systems)
  • Real depth in at least one accelerator ecosystem (CUDA/GPU, TPU, or Trainium/AWS Neuron) and genuine appetite to learn the others
  • A track record of defining and using engineering metrics to drive improvement: you've built dashboards, set SLOs on developer workflows, or led initiatives that measurably improved engineering velocity
  • Experience driving technical alignment across organizational boundaries, advocating for your team's needs while contributing to shared infrastructure
  • Strong written and verbal communication, and the ability to influence technical direction without formal authority

Also a plus

  • Experience with ML compiler toolchains (XLA, Triton, NeuronX) or accelerator driver/firmware management at scale
  • Background building or running shared development environments (devboxes, remote development, ephemeral environments) for hardware-dependent workflows
  • 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

What the job covers

  • 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 initiatives, contributing Inference-specific 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

Tools and skills named

Models & research
  • Inference10×
  • Machine learning4×
  • GPU3×
  • CUDA2×
Cloud & infra
  • CI/CD3×
  • AWS
  • Distributed systems
  • Kubernetes
Product & design
  • Roadmap2×
Ways of working
  • Code review
  • Testing

Words the posting leans on

  • technical12×
  • inference9×
  • infrastructure8×
  • development7×
  • engineering7×
  • toolchains7×
  • accelerator6×
  • developer productivity6×
  • experience6×
  • platform6×
  • build5×
  • engineer5×
  • systems5×
  • workflows5×
  • org4×
  • shared4×

Counted from the posting after the mission statement and the legal notices are set aside. The ones near the top are the ones a screener is looking for.

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How this page was made

An automated read of a public job posting, fetched November 2, 2026 and last changed by Anthropic on August 27, 2026. Every list above is pulled from the posting’s own sentences — nothing rewritten, nothing added, no judgment about the role or the company. Counts and seniority are read off the text by rule, so they can be wrong where the posting is unusual. The original is the only thing that binds. Openings close without warning; check the source before spending an evening on it.