Jobs/San Francisco/Simulation Realism Engineer
San Francisco, California, United States

Simulation Realism Engineer

About the Team Our Robotics team is focused on unlocking general-purpose robotics and pushing towards AGI-level intelligence in dynamic, real-world settings. Working across the entire model stack, we integrate cutting-edge hardware and software to explore a broad range of robotic form factors.

Company
OpenAI
Compensation
$293K - $385K
Schedule
Full-Time
Role overview

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Simulation Realism Engineer at OpenAI in San Francisco. UpJobz keeps this listing high-signal for applicants targeting serious high-tech roles across the United States, Canada, and Mexico. About the Team Our Robotics team is focused on unlocking general-purpose robotics and pushing towards AGI-level intelligence in dynamic, real-world settings. Working across the entire model stack, we integrate cutting-edge hardware and software to explore a broad range of robotic form factors.

Responsibilities

Day-to-day expectations

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  • Define and operationalize realism metrics & protocols. Design experiments and automated tests to find specific areas of non-realism, quantify gaps, and track regressions over time.
  • Fine-tune engine parameters (contacts, friction, mass/density, solver settings) and object models so simulated dynamics match measured reality.
  • Evaluate, integrate, and — where necessary — extend 3rd-party physics, rendering, and sensor simulation engines (e.g., Isaac, PhysX, MuJoCo, video renderers or sensor sim frameworks). Lead vendor POCs and benchmark features to influence roadmaps.
  • Create standards, guidelines, and interactive tools for authoring and validating asset physical/visual properties; implement semi- or fully-automated pipelines to tune and optimize assets for target engines.
  • Solve technical issues for running engines in the cloud (OS, drivers, GPU), parallelize simulations (batching many runs per engine instance), and harden real-world-facing pipelines so sims can run reliably at scale.
  • Own validation campaigns (teleop, HIL regression suites, metric computation) and partner with research to close the loop between simulation improvements and downstream model performance.
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