Model Quality Software Engineer, Claude Code

Strong candidates may also have experience with:

Writing or maintaining eval/evaluation 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)

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:

$320,000

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$485,000 USD

Logistics

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.

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

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.

Are comfortable diving into unfamiliar technical domains and figuring things out quickly

Are excited to work at the boundary between engineering and AI research