Research Engineer, Performance RL
Research Engineer on the Code RL team advancing AI models' ability to write efficient code for accelerators. Requires deep expertise in accelerators like CUDA/ROCm and ML frameworks like JAX/PyTorch, plus experience across kernels, model code, and distributed systems.
About the job
Responsibilities
- Invent, design and implement RL environments and evaluations.
- Conduct experiments and shape our research roadmap.
- Deliver your work into training runs.
- Collaborate with other researchers, engineers, and performance engineering specialists across and outside Anthropic.
Requirements
- Expertise with accelerators (CUDA, ROCm, Triton, Pallas), ML framework programming (JAX or PyTorch).
- Worked across the stack – kernels, model code, distributed systems.
- Know how to balance research exploration with engineering implementation.
- Passionate about AI's potential and committed to developing safe and beneficial systems.
Nice-to-Haves
- Experience with reinforcement learning.
- Experience porting ML workloads between different types of accelerators.
- Familiarity with LLM training methodologies.
Compensation
Annual Salary: $350,000—$850,000 USD
Skills
CUDA, Rocm, Triton, Pallas, JAX, PyTorch, Reinforcement Learning, Distributed Systems, Llm Training, Kernels
Similar jobs
ML Engineering jobs
Thinking Machines Lab
San Francisco, CA
Build and operate the engineering systems that support post-training research, including reinforcement learning infrastructure, sandboxed execution, data pipelines, and agent scaffolding. The role requires strong Python and systems engineering skills, project ownership, and a relevant bachelor’s degree or equivalent experience.
Thinking Machines Lab
San Francisco, CA
Operates and improves the infrastructure powering large-scale post-training and reinforcement learning runs, partnering with researchers to debug failures, improve reliability, and automate recovery. Requires 4+ years operating distributed production systems and strong Python, Go, or C++ skills.
Thinking Machines Lab
San Francisco, CA
Research-focused engineer advancing agentic model capabilities across synthetic data, task environments, evaluations, training, and usability improvements. Requires strong Python engineering, deep learning framework experience, scalable distributed training skills, and scientific experimentation ability.
Thinking Machines Lab
San Francisco, CA
Researcher focused on scaling reinforcement learning for frontier models, with ownership spanning asynchronous RL algorithms, inference and distributed training systems, and large-scale empirical studies. Requires strong Python and deep learning experience, scalable systems debugging, and rigorous research judgment.
OpenAI
San Francisco, CA
Develop multimodal perception and authentication systems combining visual, audio, and other sensor signals for real-world AI products. The role requires machine learning expertise, practical research-to-system experience, and proficiency in Python and PyTorch with comfort in C++.