Principal Software Engineer, Simulation

About the Team

OpenAI’s research training infrastructure powers how our frontier models are trained and evaluated. The Simulation team sits at the intersection between the agentic harness that powers OpenAI’s products and the research infrastructure where GPT‑next is trained, ensuring that our model’s training environment is as realistic as possible.

This team owns the integration layer that connects our production harness capabilities into the training stack. The work is highly cross‑functional and high leverage: researchers depend on it to run experiments and evaluations reliably as well as to develop the next generation of harness capabilities. Failures in this surface can materially affect training velocity and correctness.

We’re looking for a Principal Software Engineer to lead the architecture and evolution of the Simulation Platform. You’ll own a critical interface between research and engineering, building the systems, APIs, and operational patterns that let researchers use agentic coding infrastructure safely and effectively in training environments.

This role is ideal for a senior backend or infrastructure engineer with strong technical judgment, product sense for highly technical users, and the ability to drive execution across multiple teams. The highest‑leverage work is building robust infrastructure that supports and accelerates research without compromising engineering quality.

• Design, build, and evolve the integration between the Codex harness that powers OpenAI’s products and research training infrastructure used for training GPT‑next.

• Build a platform for our LLMs to train and be evaluated in simulated environments that mimic their deployment setting as closely as possible, on every axis: agentic harness, compute substrate, timing, tools, data sources, humans in the loop, and more.

• Own major integration surfaces end‑to‑end, from architecture and API design through rollout, operations, and long‑term maintenance.

• Build reliable execution systems that can support demanding training workloads at scale.

• Partner closely with research, agent, infrastructure, and platform teams to support new training use cases and harness capabilities.

• Design clean, stable interfaces and workflows for highly technical internal users who move quickly and expect strong ergonomics.

• Prevent one‑off workarounds from becoming long‑term technical debt by establishing durable abstractions and clear ownership.

• Raise the bar for correctness, reliability, operational rigor, and engineering judgment across a critical research‑facing system.

• Have significant experience building and scaling backend or infrastructure systems in fast‑moving environments.

• Bring deep strength in API design, systems design, and engineering fundamentals.

• Are highly detail‑oriented and care deeply about correctness, reliability, and operational quality.

• Can work directly with demanding technical users while maintaining strong engineering discipline.

• Have a track record of leading cross‑functional technical efforts and creating clarity across organizational boundaries.

• Bring strong product sense and user empathy for internal platforms and developer tooling.

• Are motivated by enabling researchers and accelerating their work, rather than doing research yourself.

• Are proficient in Python and have experience with backend platform engineering;
  Rust experience is a plus.

This role is ideally based in San Francisco due to the close collaboration required with researchers and applied engineering partners.

We are an equal‑opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status,…