Software Engineer, AI for Chip Design
$266k - $468k • Remote • San Francisco • FullTime
Posted 1mo ago
Job Location
San Francisco
Tech Stack
Remote Work Policy
Fully remote
Employment Type
FullTime
Categories
AI Infrastructure Engineer
About the job
We are seeking a Software Engineer to develop the research infrastructure and tooling that will enable OpenAI models to design silicon. This role involves transforming chip-design workflows into robust environments for reinforcement learning and evaluation, and simplifying the process for researchers to conduct experiments and iterate on new ideas. You will bridge software engineering, tool integration, and open research challenges, leveraging strong coding fundamentals, clear technical judgment, and independent execution. While prior chip-design experience is beneficial, domain knowledge can be acquired alongside our hardware specialists.
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, and debug failures across tools, services, and infrastructure.
- Develop tooling and model harnesses for researchers to quickly test ideas and measure correctness and power, performance, and area (PPA).
- Collaborate with researchers and engineers to convert successful experiments into reusable systems and training workflows.
- Own ambiguous projects end-to-end, communicate progress, and use results to guide future iterations.
Requirements
- Strong software engineering fundamentals, with experience designing, implementing, and debugging reliable systems.
- Ability to work across a technology stack, investigate unfamiliar failures, and make practical tradeoffs between speed, correctness, and maintainability.
- Proven track record of independently delivering substantial software projects and ability to articulate technical decisions and their impact.
- Comfort working with researchers on evolving requirements and translating open-ended problems into functional software.
- Interest in learning how reinforcement learning and chip-design tools intersect.
- Commitment to developing safe and beneficial AI.