[Pipeline] Senior Software Engineer, Systems

Anthropic•Seattle, WA

10h•Hybrid

About The Position

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 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. Team Matching: Team matching is determined after the interview process based on interview performance, interests, and business priorities. Please note we may also consider you for different Infrastructure teams.

Requirements

Have 6+ years of software engineering experience
Have led technical projects end-to-end over multiple months, including scoping, breaking down work, and driving delivery
Have deep knowledge of distributed systems, reliability, and cloud platforms (Kubernetes, IaC, AWS/GCP)
Are strong in at least one systems language (Python, Rust, Go, Java)
Solve hard problems independently and know when to pull others in
Help teammates grow through knowledge sharing and thoughtful technical guidance
Communicate clearly in design docs, presentations, and cross-functional discussions
We require at least a Bachelor's degree in a related field or equivalent experience.

Nice To Haves

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
Technical expertise: Quickly understanding systems design tradeoffs, keeping track of rapidly evolving software systems

Responsibilities

Lead infrastructure projects from design through delivery, owning scope, execution, and outcomes
Build and maintain systems that support AI clusters at massive scale (thousands to hundreds of thousands of machines)
Partner with cloud providers and internal teams to solve compute, networking, and reliability challenges
Tackle difficult technical problems in your domain and proactively fill gaps in tooling, documentation, and processes
Contribute to operational practices including incident response, postmortems, and on-call rotations