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.
- Developer Productivity owns the end-to-end experience of how engineers and researchers at Anthropic develop, build, test, and ship code at scale — from the source control and language ecosystems that underpin our monorepo, to the build and CI infrastructure that keeps thousands of daily builds running reliably across multiple cloud providers, to the developer acceleration tooling that deeply integrates Claude into engineering workflows.
- 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.
Responsibilities
- Own the technical strategy and roadmap for your area, translating team-level goals into concrete execution plans
- Define infrastructure architecture, ensuring the hardest problems get solved — whether by you directly or by working through others
- Design and build scalable, reliable distributed infrastructure and shared libraries that support high-volume workloads across all engineering teams
- Own and evolve build environments, package management, and dependency systems to enable fast, reproducible builds
- Define and implement language ecosystem standards, tooling, and frameworks that drive developer productivity across research and production workloads
You may be a good fit if you
- Have 10+ years (not including internships or co-ops) of experience in a Software Engineer role, building and operating large-scale developer infrastructure
- Have 3+ years (not including internships or co-ops) of experience leading large scale, complex projects or teams as an engineer or tech lead
- Have deep experience with build systems, CI/CD pipelines, and/or developer tooling in a large monorepo environment
- Have strong proficiency in Python, Rust and/or Go
- Are obsessed with developer productivity and reducing friction in the software development lifecycle
- Have experience with container orchestration and infrastructure at scale
- Have excellent communication skills and enjoy supporting internal partners to improve their development experience
- Are excited about designing foundational systems and are comfortable working independently on ambiguous, high-impact technical challenges
Strong candidates may have
- Experience with CI orchestration tools (Buildkite, Jenkins, GitHub Actions, or similar) and merge queue management at scale
- Experience building or operating remote build execution systems (Bazel Remote Execution API, BuildBarn, BuildBuddy, or similar)
- Experience with Nix/NixOS/Docker and managing large image / package sets at scale
- Experience building CLI tools, developer-facing services, and GitHub API and automation workflows
- Deadline to apply: None. Applications will be reviewed on a rolling basis.
- 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.
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.
