Simulation Realism Engineer

OpenAI OpenAI · AI Frontier · San Francisco, CA · Research

Seeking a Simulation Realism Engineer to improve the accuracy of simulation environments for robotics research and product development. This role involves defining realism metrics, tuning simulation parameters, integrating third-party engines, and developing tooling and pipelines for large-scale simulation operations. The goal is to close the sim-to-real gap for AI-powered robots.

What you'd actually do

  1. 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.
  2. Fine-tune engine parameters (contacts, friction, mass/density, solver settings) and object models so simulated dynamics match measured reality.
  3. 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.
  4. 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.
  5. 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.

Skills

Required

  • Strong mathematical & physics background (rigid/soft-body dynamics, contact mechanics, or FEA)
  • Hands-on experience with production physics engines and sensor/render stacks (MuJoCo, PhysX, Isaac Sim, Newton, GPU renderers, or equivalent)
  • Experience building robust tools and processes (CI for sims, asset validation pipelines, or distributed GPU workloads)
  • Experience bridging sim and robotics (teleoperation data collection, hardware-in-the-loop regression tests, or digital twin efforts)

Nice to have

  • Reasoning about solvers and numerical stability
  • Measuring/interpreting failure modes of physics/render engines
  • Plumbing layers of the sim stack (CAD/asset import, collision/visual mesh tradeoffs, contact tuning, real-time loading, rendering pipelines)
  • Experience with cloud operations (OS, drivers, GPU)
  • Experience parallelizing simulations
  • Collaboration with researchers, SWE/RE, and vendors

What the JD emphasized

  • quantitatively real
  • close sim→real gaps
  • measurable realism metrics
  • physical systems
  • scientific rigor (measurement & validation)
  • pragmatic systems work
  • production physics engines
  • sensor/render stacks
  • measure/interpret their failure modes
  • bridge sim and robotics
  • hardware-in-the-loop regression tests
  • digital twin efforts
  • robust tools and processes
  • CI for sims
  • asset validation pipelines
  • distributed GPU workloads

Other signals

  • robotics research
  • simulation realism
  • physics engines
  • sensor simulation
  • rendering