About the job
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. The Systems engineering team owns compute uptime and resilience at massive scale, building the clusters, automation, and observability that make frontier AI research possible and safely deployable to customers.
Responsibilities
Own the technical strategy and roadmap for your area, translating team-level goals into concrete execution plans
Drive cross-team initiatives to build and scale AI clusters (thousands to hundreds of thousands of machines)
Define infrastructure architecture, ensuring the hardest problems get solved — whether by you directly or by working through others
Partner with cloud providers and internal stakeholders 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
Qualifications
Minimum
Deep expertise in distributed systems, reliability, and cloud platforms (e.g., Kubernetes, IaC, AWS/GCP)
Proficiency in at least one systems language (e.g., Python, Rust, Go, Java)
Track record of leading complex, multi-quarter technical initiatives that span multiple teams or systems
Experience setting technical direction for a team, not just executing within it
Ability to build alignment across senior stakeholders and communicate effectively at all levels
Preferred
10+ years of software engineering experience
Security and privacy best practice expertise
Experience with machine learning infrastructure like GPUs, TPUs, or Trainium, as well as supporting networking infrastructure like NCCL
Low-level systems experience, for example Linux kernel tuning and eBPF
Skill in quickly understanding systems design tradeoffs and keeping track of rapidly evolving software systems
Experience redirecting team efforts when initiatives are heading off track