Staff+ Software Engineer, Inference Runtime

Anthropic; Seattle (WA); Hybrid; USD 405,000 - 485,000; Full time

Job summary

Anthropic is looking for a Staff Engineer to lead the Inference Runtime team in Seattle, WA. You will oversee the architecture and roadmap for inference serving systems, ensuring performance and correctness across GPU, TPU, and Trainium platforms.

The ideal candidate will have a strong background in systems engineering and software development. Familiarity with high-performance distributed systems and mentoring experience is crucial. This is a hybrid position with a salary range of $405,000—$485,000.

Qualifications

• Deep background in systems engineering or ML infrastructure.
• Real depth in at least one accelerator ecosystem (CUDA/GPU, TPU, or Trainium).
• Experience with high-performance distributed systems.
• Track record of using engineering metrics for improvements.
• Strong communication skills to influence technical direction.

Responsibilities

• Set technical direction and architecture for the inference serving stack.
• Own and evolve the runtime’s interfaces and structure.
• Drive efficient accelerator usage across different platforms.
• Mentor engineers through design and code reviews.

Skills

Systems engineering

ML infrastructure

Performance profiling

Latency optimization

Software engineering experience

Technical alignment

Education

Bachelor’s degree or equivalent

Tools

CUDA/GPU

TPU

Rust

Python

Kubernetes

Job description

About the Company

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 researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the Role

Staff Engineer – Inference Runtime 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. The Staff Engineer will be the technical lead for the Inference Runtime team, owning the shared, accelerator‑agnostic core of our inference serving stack and its performance, correctness, and abstractions.

Key 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 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.

Minimum Qualifications

• Deep background in systems engineering or ML infrastructure, with the ability to go hands‑on with 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, such as setting SLOs on platform surfaces and driving measurable changes in escape rates, release times, latency, or throughput.
• Experience driving technical alignment across organizational boundaries, advocating for team needs while contributing to shared infrastructure.
• Strong written and verbal communication, and the ability to influence technical direction without formal authority.

Preferred Qualifications

• 8+ years of software engineering experience, with significant time as the 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 production as a 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.

Annual Salary

$405,000—$485,000 USD

Logistics

Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience.
Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience.
Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position.
Location‑based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
Visa sponsorship: We sponsor visas for many roles, but not all. If you receive an offer, we will make every reasonable effort to help obtain a visa.