Technical Lead Manager, Model Quality - Claude Code

About the Role

We're looking for a Technical Lead Manager to build and lead the Model Quality engineering team within Claude Code. This team sits at the intersection of engineering and research, building the eval systems, data pipelines, and experimentation infrastructure that tell us where Claude's coding capabilities excel and where they fall short, and then closing those gaps.

As TLM, you'll be hands-on, setting technical direction, reviewing designs, and shipping code alongside your team — while also hiring, coaching, and growing a group of strong senior engineers who thrive in ambiguous, high-intensity environments. You'll be the connective tissue between Claude Code product priorities and Anthropic's research org, ensuring the team is building infrastructure that actually accelerates our research loop.

What you'll do

You'll own the technical roadmap for model quality infrastructure on Claude Code, including eval frameworks, experimentation tooling, data pipelines.  You will be accountable for the reliability and correctness of systems that researchers depend on daily. You'll hire and support a team of engineers and you'll partner closely with research leadership to translate open questions into engineering priorities, and with Claude Code product to ensure capability improvements show up in the product. And you'll stay close to the code!

You may be a good fit if you

• Have led engineering teams (as a manager or tech lead) building complex infrastructure — data platforms, ML tooling, eval systems, or research computing

• Are a strong IC engineer in your own right and want to stay technical

• Have operated in high-intensity, fast-iteration environments and know how to keep a team moving without burning out

• Are comfortable navigating ambiguity across organizational boundaries — you know how to align teams with different incentives on shared goals

• Are a power user of agentic coding tools and have real intuition for where models are strong and where they break

• Care deeply about correctness and reliability, and can instill that bar in a team

• Have 8+ years of engineering experience, including 2+ leading teams

Strong candidates may also have

• Built or maintained evaluation frameworks for ML systems

• Experience with reinforcement learning infrastructure

• A background in research computing, scientific infrastructure, or ranking and recommendation systems

• Experience with production ML monitoring and observability

• A strong quantitative foundation