Software Engineer, AI for Chip Design
Build research infrastructure and tooling that enables AI models to design silicon, including reinforcement learning environments, EDA integrations, evaluations, and experiment workflows. The role requires strong software engineering fundamentals and comfort working across research, tooling, and chip-design systems.
About the job
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 enable rapid research iteration and measure correctness, 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 iteration.
Requirements
- Strong software engineering fundamentals, including designing, implementing, and debugging reliable systems.
- Ability to work across a technical stack, investigate unfamiliar failures, and make tradeoffs among speed, correctness, and maintainability.
- Experience independently delivering substantial software projects and explaining technical decisions and impact.
- Ability to work with researchers amid evolving requirements and turn open-ended problems into working software.
- Interest in learning how reinforcement learning and chip-design tools work together.
- Commitment to developing safe, beneficial AI.
Nice-to-haves
- Research infrastructure, distributed systems, experiment orchestration, or machine learning tooling experience.
- 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.
Skills
Python, Reinforcement Learning, Electronic Design Automation, Rtl, Verilog, Systemverilog, Formal Verification, Distributed Systems, Experiment Orchestration, Machine Learning, Containerization, Research Infrastructure, Model Evaluation, Physical Design
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