Research Engineer, Interpretability

Roles and 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

You may be a good fit if you:

• 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.

Strong candidates may also have experience with:

• 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

Representative Projects:

• Building Garcon, a tool that allows researchers to easily instrument LLMs to extract internal activations
• Designing and optimizing a pipeline to efficiently collect petabytes of transformer activations and shuffle them
• Profiling and optimizing ML training jobs, including multi-GPU parallelism and memory optimization
• Building a steered inference system that applies targeted interventions to model internals at scale (conceptually similar to Golden Gate Claude but for safety research)

Role Specific Location Policy:

• This role is based in the San Francisco office; however, we are open to considering exceptional candidates for remote work on a case-by-case basis.