Description
Compensation
The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. The salary range for this role is $266K – $468K, with generous equity, performance-related bonus(es) for eligible employees, and the following benefits.
Benefits include:
- Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts
- Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)
- 401(k) retirement plan with employer match
- Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)
- Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees
- 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time
- Mental health and wellness support
- Employer-paid basic life and disability coverage
- Annual learning and development stipend to fuel your professional growth
- Daily meals in our offices, and meal delivery credits as eligible
- Relocation support for eligible employees
- Additional taxable fringe benefits, such as charitable donation matching and wellness stipends
About the Team
OpenAI develops models that can reason through complex problems and hardware designed for the demands of advanced AI. The AI for Chips team applies increasingly capable AI systems to the work of semiconductor engineering to help engineers develop better chips and shorten design cycles.
About the Role
We're hiring a Software Engineer to build the research infrastructure and tooling that help OpenAI models design silicon. You will turn chip-design workflows into reliable environments for reinforcement learning and evaluation, and make it easier for researchers to run experiments and iterate on new ideas.
Responsibilities
- Build and maintain infrastructure for reinforcement learning environments, evaluations, and long-running experiments.
- Integrate electronic design automation (EDA) tools into workflows for RTL generation, verification, and physical design optimization.
- Improve experiment reliability, reproducibility, observability, and performance; debug failures across tools, services, and infrastructure.
- Develop tooling and model harnesses that let researchers test ideas quickly and measure correctness and power, performance, and area (PPA).
- Collaborate with researchers and engineers to turn successful experiments into reusable systems and training workflows.
- Own ambiguous projects end to end, communicate progress, and use results to guide the next iteration.
Requirements
- Strong software engineering fundamentals, with experience designing, implementing, and debugging reliable systems.
- Ability to work across a stack, investigate unfamiliar failures, and make practical tradeoffs between speed, correctness, and maintainability.
- Experience delivering substantial software projects and explaining technical decisions and their impact.
- Comfortable working with researchers on evolving requirements and turning open-ended problems into working software.
- Interest in learning how reinforcement learning and chip-design tools fit together.
- Care about developing safe, beneficial AI.
Nice to have
- Experience with research infrastructure, distributed systems, experiment orchestration, or ML tooling.
- Familiarity with reinforcement learning, model evaluations, or training workflows.
- Experience with Python, containerized tools, and reproducible development environments.
- Experience with RTL, Verilog/SystemVerilog, EDA tools, formal verification, or chip-design automation.