Model Quality Software Engineer

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: Model Quality Software Engineer

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

How We're Different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.

Key skills/competency

• AI systems development
• Software Engineering
• Evaluation Frameworks
• Infrastructure design
• Data Pipelines
• Python programming
• TypeScript programming
• Research collaboration
• System Reliability
• Product Intuition