Staff Data Scientist – Simulation Motion AI

Company Description

It started with a simple idea: what if surgery could be less invasive and recovery less painful? Nearly 30 years later, that question still fuels everything we do at Intuitive. As a global leader in robotic-assisted surgery and minimally invasive care, our technologies—like the da Vinci surgical system and Ion—have transformed how care is delivered for millions of patients worldwide.

We’re a team of engineers, clinicians, and innovators united by one purpose: to make surgery smarter, safer, and more human. Every day, our work helps care teams perform with greater precision and patients recover faster, improving outcomes around the world.

The problems we solve demand creativity, rigor, and collaboration. The work is challenging, but deeply meaningful—because every improvement we make has the potential to change a life.

If you’re ready to contribute to something bigger than yourself and help transform the future of healthcare, you’ll find your purpose here.

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.

Qualifications

Required Qualifications

• PhD or Master’s degree in Computer Science, robotics, machine learning, data science, electrical engineering, mechanical engineering, applied mathematics, or a related technical field.

• 9+ years of industry experience, post training, developing simulation, machine learning, robotics, computer vision, simulation, or autonomous-systems software; or 4+ years of industry experience with a PhD

• Proven hands-on experience with at least one major robotics simulation platform: NVIDIA Isaac Sim, NVIDIA Isaac Lab, MuJoCo or Equivalent physics-based robotics simulation environment

• Expertise in creating custom simulation environments, robot assets, task definitions, sensors, reward functions, termination criteria, or procedural scene-generation systems.

• Expertise with Python and modern machine-learning frameworks such as PyTorch

• Experience working with robot kinematics, dynamics, coordinate frames, calibration, trajectory representations, and closed-loop control and designing datasets and experiments for multimodal or time-series machine learning.

• Demonstrated ability to diagnose model failures using quantitative analysis rather than relying solely on aggregate success metrics.

• Experience deploying, testing, or validating models on physical robotic systems.

Preferred Qualifications

• Experience developing vision-based manipulation policies using RGB, stereo, depth, segmentation, optical flow, keypoints, or learned visual representations.

• Experience with transformer, diffusion, or vision-language-action for robotics.

• Experience with deformable-object simulation, articulated objects, fluids, cables, sutures, tissue, or other contact-rich environments.

• Experience