Product Manager, Developer Productivity

Anthropic•San Francisco, NY

1d•Hybrid

About The Position

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. 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.

Requirements

7+ years of product management experience, with deep exposure to developer tooling, build systems, CI/CD, or platform infrastructure
Experience taking technical platform products from infancy to scale—you've built something from the ground up and grown it to serve demanding internal or external engineering customers
Track record of building platform products that balance the needs of multiple engineering personas—you're comfortable making prioritization trade-offs between velocity, reliability, and security, and communicating them clearly
Ability to internalize complex technical systems (build systems, monorepos, CI pipelines, accelerator toolchains) and translate that understanding into a comprehensive product vision
Fluent across functions—you're equally credible discussing build graph optimization with engineers, developer velocity economics with leadership, and AI-agent governance with security teams
A strong thesis on how AI will reshape software development—you've thought deeply about what changes when agents write, review, and ship meaningful portions of a codebase, and you're energized by defining the tooling for that world rather than waiting for it to arrive
Scrappy and resourceful—you do what it takes to get things done in a fast-moving environment

Nice To Haves

Built or scaled developer productivity, build systems, or CI/CD platforms for large engineering organizations (e.g., Bazel, Buck, large-scale monorepos, or custom build infrastructure).
Experience defining and operationalizing engineering productivity metrics (DORA, SPACE, or custom frameworks)—and a point of view on how these metrics evolve when AI agents are in the loop.
Familiarity with accelerator toolchain ecosystems (CUDA/GPU, TPU, or AWS Neuron/Trainium) and the unique developer experience challenges of compute-intensive ML workloads.
Shipped AI-native developer tooling—code assistants, agent-based automation, or AI-integrated IDEs—and understand the governance, trust, and adoption challenges that come with it.
Scaled through hypergrowth in engineering-intensive environments (AI/ML, large-scale cloud infrastructure, or developer tools companies).
Experience with internal platform adoption—you know that the best internal tool is the one engineers actually use, and you've driven adoption through product quality rather than mandate.

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 roadmap that keeps Anthropic ahead of—not reacting to—the exponential curve of AI-assisted development.