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OpenAI's Hardware organization is building AI-native silicon and system-level solutions for advanced AI workloads. This role focuses on developing the design methodology, software abstractions, and infrastructure that enable rapid, high-confidence chip development.
You will create reusable tools and workflows that improve iteration speed and quality across the silicon development process. Working closely with architects, RTL designers, verification engineers, compiler engineers, and systems software engineers, you'll translate evolving hardware needs into robust software solutions.
Key responsibilities include:
- Developing and scaling design methodologies for first-party chip development
- Creating abstractions for consistent representation of hardware structures, configurations, experiments, and results across tools
- Automating high-value engineering workflows and improving reproducibility, observability, and testability
- Partnering across teams to gather requirements and implement solutions
- Using methodology and tooling to identify design risks early and accelerate iteration
- Contributing across multiple aspects of software and hardware development as the silicon program evolves
Ideal candidates have strong software engineering skills in C++, Python, Rust, or similar systems-oriented languages. Experience building engineering infrastructure, developer tools, simulation frameworks, EDA/CAD tooling, compilers, or verification systems is valuable. You should understand digital design and computer architecture well enough to translate silicon-development needs into useful software abstractions, with experience integrating RTL and IP components into SoCs. You've designed APIs, object models, or data models for complex systems and care about extensibility, correctness, and usability. You're comfortable automating multi-stage workflows, building regression systems, and improving reproducibility. You can reason across architecture, design, verification, performance modeling, and software boundaries, and thrive in rapidly evolving environments while balancing immediate needs with longer-term platform quality.
