Model Quality Software Engineer, Claude Code
AnthropicSan Francisco, CA | New York City, NYPosted 7 April 2026
Tech Stack
Job Description
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
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.
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
You may be a good fit if you:
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
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)
Python and TypeScript
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:
$320,000
$485,000 USD
Logistics
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 qu ... (truncated, view full listing at source)
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