Product Manager, Developer Productivity at Anthropic

San Francisco, CA

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As a Product Manager focused on Developer Productivity at Anthropic, you will partner with Infrastructure, Inference, Research, and Product Engineering to build the systems that determine how thousands of engineers and researchers develop, build, test, and ship code—the foundation on which every model, evaluation, and product feature depends. Your work will directly impact engineering velocity across the company by defining abstractions, measuring friction, and shaping toolchains that enable AI‑assisted development. You will drive strategy for acceleration tooling, manage trade‑offs between velocity and reliability, and champion productivity metrics that capture human‑agent collaboration. The role involves working closely with cross‑functional engineering leads to scale build, CI, and test infrastructure and to develop a roadmap for AI‑enabled developer acceleration.

Requirements

Skills

  • 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
  • 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

Technologies

MonorepoBuild systemsCI/CDGPUTPUTrainiumCUDAAWS NeuronDORASPACEAI agentsAI‑native acceleration

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