5 months ago
Base Salary
$500k - $850k/yr
Responsibilities
- Build and improve RL training infrastructure used by researchers.
- Identify and remove bottlenecks through debugging, profiling, and rearchitecting.
- Partner with researchers and adjacent teams such as inference and sandboxing to develop productivity-enhancing tooling.
- Own the reliability and performance of research runs end-to-end.
- Contribute to design decisions shaping RL at scale at Anthropic.
Requirements
- Strong software engineering fundamentals and a track record of building performant, reliable systems.
- Experience with ML infrastructure, distributed systems, or research tooling.
- Ability to work across the stack, from low-level performance work to RL algorithms.
- Strong collaboration, communication, ownership, and iterative delivery skills.
- Experience with large-scale distributed training in RL, pre-training, or post-training is a strong plus.
- Familiarity with JAX, PyTorch, or similar ML frameworks is a strong plus.
- Bachelor’s degree in a relevant field, or an equivalent combination of education, training, and/or experience.
Benefits
- Annual compensation range of $500,000-$850,000 USD.
- Hybrid policy requiring staff to work from an office at least 25% of the time, with some roles requiring more.
- Visa sponsorship is available, with immigration-lawyer support.
- Competitive benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and collaborative office space.
Tech Stack
Categories
About Anthropic
We're an AI research company that builds reliable, interpretable, and steerable AI systems. Our first product is Claude, an AI assistant for tasks at any scale. Our research interests span multiple areas including natural language, human feedback, scaling laws, reinforcement learning, code generation, and interpretability.