Staff Data Scientist – Simulation Motion AI

Intuitive; Peachtree Corners (GA); On-site; USD 140,000 - 210,000; Full time

Job summary

Intuitive is seeking a Staff Data Scientist - Simulation Motion AI to lead the design of scalable simulation-data systems for training, evaluating, and improving robotic policies used in safety-critical motion AI products.

You will collaborate with foundation and policy model teams, test, regulatory, product and clinical teams to generate data and test algorithms in simulation at scale for motion AI products.

Responsibilities

• Architect scalable pipelines for generating synthetic and procedurally varied robotic interaction data using Isaac Sim, Isaac Lab, MuJoCo, or similar simulators.
• Develop scalable simulation environments for task data generation, ML, and scene domain randomization strategies.
• Define data-generation curricula that progress from constrained primitive actions to long-horizon, multi-stage robotic tasks.
• Generate targeted recovery data for rare but safety-relevant conditions, including failed grasps, object displacement, camera obstruction, tracking loss, unexpected contact, and partial task completion.
• Design systems for efficiently replaying, perturbing, relabeling, and extending real-world robot trajectories in simulation.
• Establish provenance, metadata, versioning, and reproducibility standards for simulated datasets.

Tools

Isaac Sim

Isaac Lab

MuJoCo

Job description

Job Description

Primary Function of Position

As a Staff Data Scientist - Simulation Motion AI, you will lead the design of scalable simulation-data systems used to train, evaluate, and improve robotic policies for complex, safety-critical motion AI products. You will work alongside foundation and policy model teams, test, regulatory, product and clinical teams to generate data, train and test algorithms in simulation at scale for motion AI products.

Responsibilities

• Architect scalable pipelines for generating synthetic and procedurally varied robotic interaction data using Isaac Sim, Isaac Lab, MuJoCo, or similar simulators.

• Build SOTA, scalable simulation environments developing task data generation, ML, and scene domain randomization strategies covering:

  • Robot initial conditions and calibration errors

  • Camera poses, optics, lighting, and occlusions

  • Object geometry, appearance, pose, and material properties

  • Contact, friction, compliance, deformation, and force variation

  • Tool wear, latency, noise, and actuator uncertainty

  • Nominal, edge-case, failure, and recovery conditions

• Define data-generation curricula that progress from constrained primitive actions to long-horizon, multi-stage robotic tasks.

• Generate targeted recovery data for rare but safety-relevant conditions, including failed grasps, object displacement, camera obstruction, tracking loss, unexpected contact, and partial task completion.

• Design systems for efficiently replaying, perturbing, relabeling, and extending real-world robot trajectories in simulation.

• Establish provenance, metadata, versioning, and reproducibility standards for simulated datasets.