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 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 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
- 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
- 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
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.
Come work with us!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.
Skills Required
- Deep background in systems engineering or ML infrastructure, including 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).
- Significant software engineering experience with high-performance, large-scale distributed systems serving millions of users.
- Track record of defining and using engineering metrics and SLOs to drive measurable platform improvements.
- Experience driving technical alignment across organizational boundaries and advocating for platform needs.
- Strong written and verbal communication and ability to influence technical direction without formal authority.
- Bachelor's degree or equivalent combination of education, training, and/or experience.
- 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 validation surfaces 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 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.
Anthropic Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Anthropic and has not been reviewed or approved by Anthropic.
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Healthcare Strength — Health coverage for employees and dependents is described as comprehensive across medical, dental, and vision, alongside robust mental-health resources. Feedback suggests this breadth, paired with life and income protection, is a standout element of the package.
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Parental & Family Support — Family-building support includes inclusive fertility benefits and an extended paid parental leave policy. Feedback suggests these programs are positioned as company‑wide and accessible rather than one‑off perks.
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Wellbeing & Lifestyle Benefits — Everyday support spans wellness/time‑saver stipends, education and home‑office stipends, commuter benefits, daily meals/snacks, and relocation assistance. Feedback suggests these perks meaningfully supplement core pay and healthcare.
Anthropic Insights
What We Do
Anthropic is an AI safety and research company that’s working to build reliable, interpretable, and steerable AI systems. Our research interests span multiple areas including natural language, human feedback, scaling laws, reinforcement learning, code generation, and interpretability.
