About the job
We are seeking a Senior Applied Scientist to join our Robotics Simulation (AR R&D) team developing simulation environments and foundational models for robotics. You will lead the development of 3D physics-based simulation environments and tools to automate reusable sim asset creation for training large-scale machine learning models.
Responsibilities
- Mentor a team of scientists on Robotic Simulation best practices
- Establish processes for developing simulations for reinforcement learning, closed-loop simulations and synthetic data generation
- Create frameworks for incorporating essential robotics features, including accurate modeling of sensors, actuators, and controllers into simulations
- Build real-to-sim workflows for dynamic environments and robotics tasks
- Direct the implementation of simulation features to minimize sim-to-real gaps through domain randomization and system identification
- Create asset tool chains supporting industry-standard formats (URDF, MJCF, USD)
- Collaborate closely with a team of ML researchers to enable large-scale robotics training pipelines
Qualifications
Minimum
- PhD, or Master's degree and 6+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning
Preferred
- Broad experience across a range of physics simulators (IsaacSim, IssacLab, MuJoCo, Drake, etc.), both as a sim “power-user” and technical developer
- First-hand experience in sim2real transfer (i.e. developing learned policies in sim and successfully getting them to work on real robots)
- Experiencing in closing sim2real gaps both in terms of visual fidelity and physics fidelity
- Deep expertise in robotics (controls, motion planning, perception, etc.), ideally both in the context of manipulation and locomotion
- Deep expertise in reinforcement learning, especially in the context of robotics
- Experience with VLAs and using simulation for data generation and benchmarking
- Experience with ROS2