Job description

About Anthropic 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. About the role Anthropic's Infrastructure organization is foundational to our mission of developing AI systems that are reliable, interpretable, and steerable. The systems we build determine how quickly we can train new models, how reliably we can run safety experiments, and how effectively we can scale Claude to millions of users — demonstrating that safe, reliable infrastructure and frontier capabilities can go hand in hand. Node Infra owns the full lifecycle of accelerator capacity at Anthropic. We ingest and provision compute from all major cloud providers and from datacenters custom-built for Anthropic, stand up and scale the clusters behind one of the industry's largest AI compute fleets, and build the health, diagnostics and repair automation that keep every GPU, TPU and Trainium node in the fleet usable and ready to power Anthropic's frontier AI research. Key responsibilities Own the technical strategy and roadmap for node lifecycle management - ingestion, bring-up, health checking, and automated repair Drive cross-team initiatives to build and scale AI clusters across multiple clouds and accelerator families Design and operate the systems that detect, isolate, and remediate unhealthy hardware automatically, driving up fleet MTBI and minimizing stranded capacity Define infrastructure architecture, ensuring the hardest problems get solved - whether by you directly or by working through others Work closely with cloud providers and internal research/inference/product teams to shape long-term compute, data, and infrastructure strategy Establish and evolve operational excellence practices (incident response, postmortem culture, on-call) Support the growth of engineers around you through technical mentorship and coaching Minimum qualifications Deep expertise in distributed systems, reliability, and cloud platforms (e.g., Kubernetes, IaC, AWS/GCP/Azure) Strong proficiency in at least one systems language (e.g., Rust, Go, or Python), IaC proficiency with Terraform. Hands-on experience with machine learning accelerators (GPUs, TPUs, or Trainium) Track record of leading complex, multi-quarter technical initiatives that span multiple teams or systems Ability to build alignment across senior stakeholders and communicate effectively at all levels Preferred qualifications 10+ years of software engineering experience, including time as a technical lead setting direction for a team Experience managing large scale compute infrastructure at hyperscale (10K+ nodes), including capacity management and efficiency Depth in one or more of: Kubernetes internals (scheduler, autoscaler, kubelet, Karpenter), cluster orchestration systems (Mesos, Borg-like), or node provisioning pipelines Low-level systems experience: kernel, virtualization, device drivers, firmware, or hardware health/diagnostics daemons Familiarity with high-performance networking (EFA, RDMA, InfiniBand) for distributed ML workloads. Demonstrated ownership of production reliability for high-throughput, latency-sensitive systems Contributions to relevant open-source projects (Kubernetes, Linux kernel, container runtimes, etc.) Skill in quickly understanding systems design tradeoffs and keeping track of rapidly evolving software systems 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. Annual Salary: $320,000 — $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 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. How we're different 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.

Requirements

Must-haves

Own the technical strategy and roadmap for node lifecycle management - ingestion, bring-up, health checking, and automated repair
Drive cross-team initiatives to build and scale AI clusters across multiple clouds and accelerator families
Design and operate the systems that detect, isolate, and remediate unhealthy hardware automatically, driving up fleet MTBI and minimizing stranded capacity
Define infrastructure architecture, ensuring the hardest problems get solved - whether by you directly or by working through others
Work closely with cloud providers and internal research/inference/product teams to shape long-term compute, data, and infrastructure strategy
Establish and evolve operational excellence practices (incident response, postmortem culture, on-call)
Support the growth of engineers around you through technical mentorship and coaching
Deep expertise in distributed systems, reliability, and cloud platforms (e.g., Kubernetes, IaC, AWS/GCP/Azure)
Strong proficiency in at least one systems language (e.g., Rust, Go, or Python), IaC proficiency with Terraform.
Hands-on experience with machine learning accelerators (GPUs, TPUs, or Trainium)
Track record of leading complex, multi-quarter technical initiatives that span multiple teams or systems
Ability to build alignment across senior stakeholders and communicate effectively at all levels
10+ years of software engineering experience, including time as a technical lead setting direction for a team

Nice-to-haves

Experience managing large scale compute infrastructure at hyperscale (10K+ nodes), including capacity management and efficiency
Depth in one or more of: Kubernetes internals (scheduler, autoscaler, kubelet, Karpenter), cluster orchestration systems (Mesos, Borg-like), or node provisioning pipelines
Low-level systems experience: kernel, virtualization, device drivers, firmware, or hardware health/diagnostics daemons
Familiarity with high-performance networking (EFA, RDMA, InfiniBand) for distributed ML workloads.
Demonstrated ownership of production reliability for high-throughput, latency-sensitive systems
Contributions to relevant open-source projects (Kubernetes, Linux kernel, container runtimes, etc.)
Skill in quickly understanding systems design tradeoffs and keeping track of rapidly evolving software systems
The annual compensation range for this role is listed below.
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.
How we're different
Come work with us!

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aigokubernetesawsgcpazurerustpythonmachine learningml

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Vision

Parental Leave