AI-Driven Chip Design Research Engineer

Precision Labs; San Francisco, Northern (CA, KY); Hybrid; USD 180,000 - 240,000; Full time

Job summary

OpenAI in San Francisco is hiring a Research Engineer to advance chip-design problems using reinforcement learning, tool use, and evaluation. You’ll own experiments from idea to implementation and analysis, building environments, running training, analyzing failures, and producing reliable, reusable code and workflows.

You will thrive if you have strong coding fundamentals and can work independently on ambiguous problems, with experience in RL, model evaluations, and automated evaluation

Qualifications

• Strong programming and debugging skills; turn technical ideas into working software.
• Experience with reinforcement learning, model evaluations, or applied ML research.
• Experience building tool-using agents, reward functions, or automated evaluation systems.

Responsibilities

• Build RL environments and evaluations for 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 improvements 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 key outcomes.
• Turn successful experiments into reusable research code and training workflows with researchers and engineers.

Skills

Programming

Debugging

Reinforcement learning

Model evaluations

Experiment design

Tooling

Communication

Tools

RTL

Verilog/SystemVerilog

EDA tools

Chip-design automation

Job description

OpenAI in San Francisco is hiring a Research Engineer to advance chip-design problems using reinforcement learning, tool use, and evaluation. You’ll own experiments from idea to implementation and analysis, building environments, running training, analyzing failures, and producing reliable, reusable code and workflows.

You will thrive if you have strong coding fundamentals and can work independently on ambiguous problems, with experience in RL, model evaluations, and automated evaluation