Research Engineer, Interpretability

Job summary

Responsibilities

When you see what modern language models are capable of, do you wonder, "How do these things work? How can we trust them?"

• Build and maintain the specialized inference and training infrastructure that powers interpretability research - including instrumented forward/backward passes, activation…
• 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

What they're looking for

• 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

Tech stack

• Python
• Rust
• Go
• Java
• Transformers
• PyTorch
• CUDA
• JAX