Anthropic· Product Management, Support, & Operations· San Francisco, CA | New York City, NY
Product Manager, Developer Productivity
Classified Tasks (22)
Automate 0%Augment 36%Human-Only 64%
Augment (8)
AI assists, human decides
Own the source control and language ecosystems that underpin the monorepo
technical
Own the build and CI infrastructure that keeps thousands of daily builds running reliably across multiple cloud providers
technical
Own the acceleration tooling that integrates Claude into engineers' workflows
technical
Define the abstractions for how code moves from idea to production
technical
Establish metrics that surface friction before it compounds
analytical
Gather and synthesize needs of internal customers across Research, Inference, Infrastructure, and Product
communication
Ensure the outer loop (review, validation, deployment) does not become a bottleneck as Claude handles more of the inner loop
operational
Establish and champion productivity metrics that capture human-agent collaboration effectiveness, toil eliminated, and time-to-confident-ship
analytical
Human-Only (14)
Requires human judgment
Partner with Infrastructure, Inference, Research, and Product Engineering to build systems that determine how engineers and researchers develop, build, test, and ship code
leadership
Partner with Developer Productivity engineering teams to own the end-to-end developer experience
leadership
Make trade-offs that keep a rapidly scaling engineering organization shipping with confidence
leadership
Drive the evolution of the developer platform as AI agents move from autocomplete to autonomous collaborators
leadership
Define what developer productivity means when code is written, tested, and reviewed by Claude
analytical
Define and own the strategy and roadmap across build systems, CI/CD pipelines, developer environments, accelerator toolchain management (GPU, TPU, Trainium), and the AI-native acceleration layer
leadership
Define and iterate on the developer experience model, including workflows, tooling primitives, and feedback loops for human-AI collaboration on code
technical
Partner with engineering leads to design build, CI, and test infrastructure that scales non-linearly with engineering headcount
operational
Drive product strategy and roadmap for developer acceleration features such as AI-assisted code review, agent-driven test generation, and automated dependency management
leadership
Design and establish governance frameworks that let teams safely delegate work to autonomous systems
leadership
Own the trade-off framework between velocity, reliability, security, and cost
leadership
Make transparent prioritization decisions about where to invest in human workflows versus agent workflows
leadership
Communicate prioritization decisions and trade-offs clearly to senior leadership
communication
Build a 2–3 year vision for where developer tooling is headed and translate it into a roadmap
leadership
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 As a Product Manager focused on Developer Productivity, you'll partner with Infrastructure, Inference, Research, and Product Engineering to build the systems that determine how thousands of engineers and researchers at Anthropic develop, build, test, and ship code—the foundation on which every model, evaluation, and product feature depends: Partner with Developer Productivity engineering teams to own the end-to-end developer experience—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 acceleration tooling that deeply integrates Claude into every engineer's workflow. Your work directly impacts engineering velocity across the entire company: defining the abstractions for how code moves from idea to production, establishing the metrics that surface friction before it compounds, and making the trade-offs that keep a rapidly scaling engineering organization shipping with confidence. You'll drive the evolution of our developer platform through a fundamental shift in how software gets built—as AI agents move from autocomplete to autonomous collaborators, the definition of "developer" is changing, and our tooling, governance, and workflows must change with it. You'll be defining what developer productivity means when a meaningful share of code is written, tested, and reviewed by Claude itself. You will define and own the strategy and roadmap across build systems, CI/CD pipelines, developer environments, accelerator toolchain management (GPU, TPU, Trainium), and the AI-native acceleration layer that makes Anthropic the most productive place in the world to build frontier AI. Responsibilities: Deeply understand the needs of internal customers across Research, Inference, Infrastructure, and Product—from researchers iterating on training code who need fast, reproducible builds to inference engineers managing compute-intensive toolchains with strict compatibility constraints. Define and iterate on the developer experience model: the workflows, tooling primitives, and feedback loops that govern how engineers and AI agents collaborate on code—including how we measure productivity when the unit of work is no longer a human typing. Partner with engineering leads to design build, CI, and test infrastructure that scales non-linearly with engineering headcount—ensuring that as Claude takes on more of the inner loop, the outer loop (review, validation, deployment) doesn't become the new bottleneck. Drive product strategy and roadmap for developer acceleration, including AI-assisted code review, agent-driven test generation, automated dependency management, and the governance frameworks that let teams safely delegate work to autonomous systems. Own the trade-off framework between velocity, reliability, security, and cost—making transparent prioritization decisions about where to invest in human workflows versus agent workflows, and communicating them clearly to senior leadership. Establish and champion the productivity metrics that matter in an AI-native engineering org—moving beyond commits and cycle time to measures that capture human-agent collaboration effectiveness, toil eliminated, and time-to-confident-ship. Build conviction about where developer tooling is headed on a 2–3 year horizon, and translate that into a r