Model Quality Software Engineer, Claude Code

Anthropic•San Francisco, CA

2d•Hybrid

About The Position

We're looking for a Software Engineer to work at the intersection of engineering and research on the Claude Code team. In this role, you'll collaborate directly with Anthropic's researchers to improve Claude’s coding capabilities through tooling, infrastructure, and evaluations. You'll build systems that help us understand where Claude Code excels and where it falls short—and then help close those gaps. We're looking for engineers who can build robust, complex systems and who thrive in fast-paced, high-intensity environments. You'll take ambiguous problems and turn them into reliable infrastructure that accelerates our research.

Requirements

Have built and owned complex systems—pipelines, infrastructure, or software that orchestrates many components and handles significant state and logic
Thrive in high-intensity environments with fast iteration cycles
Take full ownership of problems and drive them to completion independently
Are a power user of agentic coding tools and have strong intuition about model capabilities and limitations
Are comfortable diving into unfamiliar technical domains and figuring things out quickly
Care deeply about correctness and reliability in the systems you build
Are excited to work at the boundary between engineering and AI research
Have at least 5 years of work experience
Python and TypeScript

Nice To Haves

Writing or maintaining eval frameworks
Reinforcement learning systems
Working in high-performance, demanding environments—trading firms, quant funds, competitive research labs, or fast-moving startups where intensity is the norm
Have research computing or scientific infrastructure background
Have a strong quantitative foundation (math, physics, or related fields)

Responsibilities

Design and build eval systems that measure model capabilities across diverse coding tasks
Build tooling and infrastructure that enables researchers to run experiments at scale
Develop pipelines for data collection, processing, and analysis
Create internal tools that improve researcher productivity and accelerate iteration cycles
Serve as a bridge between product and research—bring strong product intuition to inform which capabilities matter most
Work closely with researchers to translate research questions into engineering solutions
Own systems end-to-end—from design through production reliability