Aimed at mid-level product managers who have scaled developer tooling in fast-growing engineering environments and have a strong thesis on AI's impact on software development.
Derived from job-description analysis by Serendipath's career intelligence engine.
Original posting from Anthropic via Greenhouse
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 roadmap that keeps Anthropic ahead of—not reacting to—the exponential curve of AI-assisted development.
You may be a good fit if you have:
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
Strong candidates may have:
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.
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.
Annual Salary:$385,000—$595,000 USDLogistics
Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience
Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience
Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position
Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.
We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes represent
Source: Anthropic careers (Greenhouse)