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Overview
Join OpenAI's Robotics team as a Simulation Infrastructure Engineer to develop automated simulation systems for model training and evaluation, enhancing AI capabilities in real-world robotics. OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.
What you'll do
- •Build and maintain presubmit checks, continuous integration and deployment pipelines for simulation code, environments, and tasks so simulation artifacts are testable, versioned, and reproducible.
- •Implement end-to-end automation to run model evaluation in sim (SIL) and orchestrate HIL runs; compute realism and task metrics, generate dashboards and alerts, and ensure evaluation is repeatable and auditable.
- •Create robust APIs and connectors so research, training, and data-collection systems can schedule, seed, and evaluate batches of simulations; support RL rollouts, imitation-data collection, and presubmit model checks.
- •Build scheduling, batching and orchestration for running very large numbers of concurrent rollouts (target tens of thousands of rollouts / large RL workloads), solve engine-level scaling (parallelization, batching multiple runs per engine), and optimize cloud/GPU runtime reliability.
- •Produce metrics and tooling for measuring simulation health, throughput, fidelity regressions, and cost; create presubmit / canary tests that catch sim regressions early.
- •Implement artifact versioning, environment immutability (images / asset versions), experiment provenance, and policies for resource quotas and cost control across the sim farm.
- •Work closely with Sim Environments, Sim Realism, research, and ops to close the loop—ensuring simulation improvements directly translate into better model evaluation and training results.