anthropic
Remote-Friendly (Travel-Required) | San Francisco, CA | Seattle, WA | New York City, NY · Remote-Friendly (Travel-Required) | San Francisco
Posted Jun 12, 2026
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.
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 a technical lead for Inference Runtime: the team that owns the shared, accelerator-agnostic core of our inference serving stack, whose performance, correctness, and abstractions every accelerator builds on.
This is a senior IC role with broad technical ownership. You'll set technical direction for the runtime's architecture, its release and validation systems, and the workflows engineers use to develop on top of it. You will partner across Inferencing to make hard calls on boundaries, 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, representing the team in cross-org efforts spanning serving, scaling, and accelerator teams.
This role is for someone who has been the technical anchor of a platform with many internal consumers, who thinks in systems and feedback loops, and who gets real satisfaction from building abstractions that hold up as the system scales another order of magnitude.
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
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
Have significant software engineering experience, with a strong background in high-performance, large-scale distributed systems serving millions of users
A track record of defining and using engineering metrics to drive improvement: you've set SLOs on platform surfaces, and driven escape rates, release times, latency, or throughput in a measurable direction
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
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
The annual compensation range for this role is listed below.
For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.
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 do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.
We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.
Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.
We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.
The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.
Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.
