# Simulation Environments Engineer

**Company:** [OpenAI](https://hotfix.jobs/companies/openai)
**Location:** San Francisco, CA
**Role:** DevOps / SRE
**Salary:** $230k – $385k/yr
**Experience:** 5+ years
**Skills:** Python, C++, Rust, Kubernetes, CI/CD, ci systems, distributed task queues, rl tooling, gpu orchestration, hil, sil
**Posted:** 2026-07-22

> Build and maintain CI/CD pipelines, orchestration, and automation for large-scale robotics simulation (SIL/HIL) to support model training, evaluation, and RL workloads at OpenAI. Requires strong infra, distributed systems, and Python/C++/Rust experience.

## Job Description

## Responsibilities
- 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.

## Requirements
- Deep software engineering & infra experience: built CI/CD at scale, authored reliable pipelines, and shipped production services that coordinate many moving parts.
- Comfortable with distributed compute and cloud GPU workloads: know how to get many sims running concurrently (scheduling, batching, GPU orchestration) and optimize throughput/cost.
- Built or maintained HIL/SIL workflows or other sim↔hardware integrations and understand the operational challenges of bridging software and hardware testbeds.
- Strong with automation, observability and metrics: enjoy instrumenting systems, defining meaningful KPIs, and surfacing regressions early.
- Can design APIs and developer ergonomics so research and SWE teams can easily submit jobs, reproduce experiments, and interpret results.
- Experience with Python/C++/Rust, container orchestration (Kubernetes), distributed task queues, and CI systems.
- Enjoy collaborating across teams to turn experimental simulation work into dependable production tooling.

## Nice-to-Haves
- Worked with RL tooling, task generators, or large-scale data pipelines.

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