Research Engineer (Performance Reinforcement Learning)

Join Anthropic, a leading AI research and development company, as a Research Engineer in the Code RL team. In this role, you will advance our models' ability to safely write correct, fast code for accelerators. You will design and implement RL environments, conduct experiments, and collaborate with other researchers and engineers. Anthropic offers a comprehensive benefits package, including health insurance, paid parental leave, flexible paid time off, and competitive salary and equity packages.

• Invent, design, and implement reinforcement learning environments and evaluations to advance models' ability to write correct, fast code for accelerators.
• Conduct experiments and shape the research roadmap, delivering work into training runs and collaborating with other researchers and engineers.
• Work across the stack, from kernels to model code, and balance research exploration with engineering implementation to develop safe and beneficial AI systems.
• Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience
• Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience
• Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position
• Have worked across the stack – kernels, model code, distributed systems
• Are passionate about AI's potential and committed to developing safe and beneficial systems
• Know how to balance research exploration with engineering implementation
• Have expertise with accelerators (CUDA, ROCm, Triton, Pallas), ML framework programming (JAX or PyTorch)
• Experience porting ML workloads between different types of accelerators
• Familiarity with LLM training methodologies
• We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed
• Experience with reinforcement learning
• Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work
• We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team