Software Engineer, AI for Chip Design

Role overview

• check_circle Build and maintain infrastructure for reinforcement learning environments, evaluations, and long-running experiments.
• check_circle Integrate electronic design automation (EDA) tools into workflows for RTL generation, verification, and physical design optimization.
• check_circle Improve experiment reliability, reproducibility, observability, and performance; debug failures across tools, services, and infrastructure.
• check_circle Develop tooling and model harnesses that let researchers test ideas quickly and measure correctness and power, performance, and area (PPA).
• check_circle Collaborate with researchers and engineers to turn successful experiments into reusable systems and training workflows.
• check_circle Own ambiguous projects end to end, communicate progress, and use results to guide the next iteration.
• check_circle Have strong software engineering fundamentals, with experience designing, implementing, and debugging reliable systems.
• check_circle Can work across a stack, investigate unfamiliar failures, and make practical tradeoffs between speed, correctness, and maintainability.
• check_circle Have independently delivered substantial software projects and can explain your technical decisions and their impact.
• check_circle Are comfortable working with researchers on evolving requirements and turning open-ended problems into working software.
• check_circle Are interested in learning how reinforcement learning and chip-design tools fit together.
• check_circle Care about developing safe, beneficial AI.

Preferred qualifications

• 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.

About the company