Anthropic
Research Engineer, Interpretability
About this role
Anthropic seeks a Research Engineer to build infrastructure powering interpretability research on large language models. You'll develop specialized tools for understanding how AI systems work, from training and inference stacks to activation analysis, directly supporting AI safety efforts.
What you'll do
- Build and maintain specialized inference and training infrastructure for interpretability research, including instrumented passes and steering vector application
- Identify and resolve scaling bottlenecks through profiling and optimization
- Design tools and abstractions enabling researchers to experiment efficiently
- Integrate interpretability research into production safety audits with high reliability standards
- Work across the full stack from model internals to user-facing research tooling
- Collaborate with researchers to translate research needs into engineering solutions
What they're looking for
- Software engineering (5-10+ years)
- Python proficiency and one additional language (Rust, Go, Java)
- Distributed systems optimization
- Performance profiling and bottleneck analysis
- Machine learning infrastructure
- Quick learning across unfamiliar technical domains
- Prioritization and decision-making under ambiguity
- Cross-functional collaboration
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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
- Can you describe a time you built infrastructure or tooling that enabled researchers or domain experts to work more effectively? How did you approach understanding their needs?
- Tell us about your experience optimizing performance in large-scale systems. What bottlenecks have you identified and resolved, and how did you measure success?