Software Engineer, AI for Chip Design

OpenAI; United States; Remote; USD 140,000 - 200,000; Full time

Job summary

OpenAI is seeking a Software Engineer to build research infrastructure and tooling for silicon design. You will create reliable environments for reinforcement learning and evaluation, and integrate EDA tools for RTL generation and physical design optimization.

You will improve experiment reliability, observability, and performance across tools and infrastructure, while developing model harnesses to test ideas and measure PPA.

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.

Job description

About the Team

OpenAI develops models that can reason through complex problems and hardware designed for the demands of advanced AI. AI for Chips connects these efforts: applying increasingly capable AI systems to the work of semiconductor engineering. Our goal is to help engineers develop better chips and shorten design cycles. This work brings research, model training, and hardware expertise together to build tools that engineers can use on real designs, with correctness and measurable performance at the center.

About the Role

We’re hiring a Software Engineer to build the research infrastructure and tooling that help OpenAI models design silicon. You’ll 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. You’ll move between software engineering, tool integration, and open research problems. We value strong coding fundamentals, clear technical judgment, and independent execution. Prior chip-design experience is helpful, but you can learn the domain alongside the team’s hardware specialists. In this role, you will: 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. You might thrive in this role if you: Have strong software engineering fundamentals, with