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Posted 2 days ago

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MTS, Simulation Infrastructure Engineer

Bay AreaOn-siteFull-time

AI Summary

What You’ll DoInfrastructureBuild core simulation-centered infrastructure for robotics model development, evaluation, and iteration.Design scalable systems for creating, running, and managing diverse simulation workloads across tasks, environments, and robots.Develop reusable environment frameworks, task interfaces, and tooling that make simulation workflows more reliable and observable.Simulation EnvironmentBuild sim-ready digital twin environments for real work assets, scenes, and tasks.Tune p

About this role

What You’ll Do

Infrastructure

  • Build core simulation-centered infrastructure for robotics model development, evaluation, and iteration.

  • Design scalable systems for creating, running, and managing diverse simulation workloads across tasks, environments, and robots.

  • Develop reusable environment frameworks, task interfaces, and tooling that make simulation workflows more reliable and observable.

Simulation Environment

  • Build sim-ready digital twin environments for real work assets, scenes, and tasks.

  • Tune physics, rendering, geometry, robot settings to reduce sim-to-real gap.

  • Work closely with simulation engine, rendering, asset, AI, and robotics teams to improve realism, coverage, and usefulness of simulation.

What You’ll Bring

  • Strong software engineering skills in Python, C++, or similar language.

  • Experience building simulation systems, robotics infrastructure, evaluation platforms, or large-scale experimentation tools.

  • Strong understanding of software architecture, data pipelines, distributed systems, and production engineering practices.

  • Ability to work across robotics, simulation, ML, and infrastructure teams to turn ambiguous needs into robust systems.

Bonus Points

  • Built closed-loop or log-replay evaluation at scale.

  • Experience with robotics simulation tools such as genesis-world, Isaac Sim, MuJoCo, Newton, Drake, Gazebo, or similar platforms.

  • Experience with synthetic data, scene generation, asset pipelines, sim-to-real, or robotics benchmarks.

  • Experience with robot manipulation, mobile manipulation, humanoids, or embodied AI systems.

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