About this role
OpenAI develops reasoning models and hardware designed for advanced AI, connecting these efforts through the AI for Chips initiative to apply artificial intelligence to semiconductor engineering. The team builds tools that engineers can use on real designs with a focus on correctness and measurable performance, aiming to shorten design cycles.
Incoming team members own experiments from initial idea to implementation and analysis, building environments and evaluations while running training and investigating failures. Success in the role requires strong coding fundamentals, careful experimental judgment, and the capacity to make independent progress while collaborating across research, software, and hardware disciplines.
What you will do
- Build reinforcement learning environments and evaluations for tasks including RTL generation, design verification, and physical design optimization.
- Develop and test approaches that help models utilize chip-design tools to improve power, performance, and area while maintaining 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 reflecting target outcomes.
- Turn successful experiments into reusable research code and training workflows in close collaboration with researchers and engineers.
What you need
- Possess strong programming and debugging skills with a track record of turning technical ideas into working software.
- Bring background in reinforcement learning, model evaluations, post-training, or other applied machine learning research.
- Demonstrate experience building tool-using agents, reward functions, or automated evaluation systems.
- Form clear hypotheses, design useful experiments, and distinguish meaningful results from noise or evaluation errors.
- Work independently on ambiguous problems and make practical decisions regarding what to build or test next.
- Communicate progress clearly and collaborate well across research, software, and hardware domains.
Nice to have
- Familiarity with experiment orchestration, distributed training, or research infrastructure.
- Experience with RTL, Verilog/SystemVerilog, EDA tools, formal verification, or chip-design automation.
Benefits
- Offers Equity
Skills and tech stack
Questions candidates ask
What is the primary focus of the AI for Chips team?
The team applies increasingly capable AI systems to the work of semiconductor engineering. The goal is to help engineers develop better chips and shorten design cycles.
Do I need prior chip-design experience to apply?
Prior chip-design experience is helpful, but candidates can learn the domain alongside the team's hardware specialists.
What salary range is offered for this role?
The position offers a salary range of $380K to $500K along with equity.