Anthropic
Model Performance Software Engineer, Claude Code
About this role
Anthropic seeks a Staff Software Engineer to lead technical strategy for the Claude Code team, architecting evaluation systems and research infrastructure that enable rapid model capability improvements. You'll operate as a senior technical leader bridging engineering and research, owning critical systems end-to-end while mentoring engineers and influencing product direction.
What you'll do
- Set technical direction for evaluation frameworks, research infrastructure, and internal tooling across Claude Code
- Architect scalable evaluation systems that measure model coding capabilities across diverse tasks
- Design infrastructure enabling researchers to run large-scale experiments efficiently
- Identify and drive high-leverage engineering investments from conception through completion
- Serve as technical bridge between product and research, informing capability priorities
- Mentor engineers, review designs, and establish engineering standards for the team
What they're looking for
- 10+ years software engineering experience at Staff/Principal level
- Complex systems architecture and ownership (pipelines, infrastructure, platforms)
- Technical leadership and strategic direction-setting
- ML evaluation frameworks and infrastructure design
- Python and TypeScript
- Research computing and scientific infrastructure
- High-performance systems (trading, quant, research labs)
- Agentic AI tools and model capabilities knowledge
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Anthropic
Anthropic builds Claude, an AI assistant, and is hiring for engineering roles across infrastructure, data systems, and security that support both AI research operations and the company's internal technology needs. The company seeks infrastructure engineers, systems integrators, data scientists, and security specialists to build production-scale systems for training data pipelines, financial operations, developer productivity measurement, research infrastructure, and server firmware security.
- Website
- anthropic.com
Likely interview questions
- Walk us through a complex system you architected and owned end-to-end. What were the highest-leverage technical decisions, and how did you handle tradeoffs between speed and durability?
- Describe your experience designing or scaling evaluation frameworks. How did you approach measuring model capabilities across diverse tasks, and how did your infrastructure evolve as requirements changed?