About the job
The Interpretability team at Anthropic is working to reverse-engineer how trained models work because we believe that a mechanistic understanding is the most robust way to make advanced systems safe. Think of us as doing 'neuroscience' of neural networks using 'microscopes' we build - or reverse-engineering neural networks like binary programs.
Responsibilities
Build and maintain the specialized inference and training infrastructure that powers interpretability research - including instrumented forward/backward passes, activation extraction, and steering vector application
Resolve scaling and efficiency bottlenecks through profiling, optimization, and close collaboration with peer infrastructure teams
Design tools, abstractions, and platforms that enable researchers to rapidly experiment without hitting engineering barriers
Help bring interpretability research into production safety audits - with real deadlines and high reliability expectations
Work across the stack - from model internals and accelerator-level optimization to user-facing research tooling
Qualifications
Minimum
Have 5-10+ years of experience building software
Are highly proficient in at least one programming language (e.g., Python, Rust, Go, Java) and productive with Python
Are extremely curious about unfamiliar domains; can quickly learn and put that knowledge to work, e.g. diving into new layers of the stack to find bottlenecks
Have a strong ability to prioritize the most impactful work and are comfortable operating with ambiguity and questioning assumptions
Prefer fast-moving collaborative projects to extensive solo efforts
Are curious about interpretability research and its role in AI safety (though no research experience is required!)
Care about the societal impacts and ethics of your work
Are comfortable working closely with researchers, translating research needs into engineering solutions
Preferred
Optimizing the performance of large-scale distributed systems
Language modeling fundamentals with transformers
High Performance LLM optimization: memory management, compute efficiency, parallelism strategies, inference throughput optimization
Working hands-on in a mainstream ML stack - PyTorch/CUDA on GPUs or JAX/XLA on TPUs
Collaborating closely with researchers and building tooling to support research teams; or directly performed research with complex engineering challenges