Research Engineer, AI for Chip Design
$380k - $500k • Remote • San Francisco • FullTime
Posted 1mo ago
Job Location
San Francisco
Tech Stack
Remote Work Policy
Fully remote
Employment Type
FullTime
Categories
AI Research Engineer
About the job
We’re hiring a Research Engineer to help OpenAI models solve chip-design problems through reinforcement learning, tool use, and evaluation. You’ll own experiments from the initial idea through implementation and analysis, building environments and evaluations, running training, investigating failures, and using the results to decide what to try next. You’ll also build the software needed to make those experiments reliable and reproducible. We value strong coding fundamentals, careful experimental judgment, and the ability to make progress independently. Prior chip-design experience is helpful, but you can learn the domain alongside the team’s hardware specialists.
Responsibilities
- Build RL environments and evaluations for tasks such as RTL generation, design verification, and physical design optimization.
- Develop and test approaches that help models use chip-design tools and improve power, performance, and area while preserving correctness.
- Design experiments, establish baselines, and measure whether improvements hold up on new tasks and designs.
- Investigate failures across model behavior, rewards, evaluation tools, and experiment infrastructure.
- Improve iteration speed through better tooling, faster evaluations, and proxy rewards that reflect the outcomes we care about.
- Turn successful experiments into reusable research code and training workflows, working closely with researchers and engineers.
Requirements
- Strong programming and debugging skills and a track record of turning technical ideas into working software.
- Experience with reinforcement learning, model evaluations, post-training, or other applied ML research.
- Experience building tool-using agents, reward functions, or automated evaluation systems.
- Ability to form clear hypotheses, design useful experiments, and distinguish meaningful results from noise or evaluation errors.
- Ability to work independently on ambiguous problems and make practical decisions about what to build or test next.
- Ability to stay close to the implementation and explain what was built, what failed, and what was learned.
- Ability to communicate progress clearly and collaborate well with people across research, software, and hardware.
- Care about developing safe, beneficial AI.
- Familiarity with experiment orchestration, distributed training, or research infrastructure (nice to have).
- Experience with RTL, Verilog/SystemVerilog, EDA tools, formal verification, or chip-design automation (nice to have).