Sr. Simulation & Synthetic Data Engineer

Intuitive•Sunnyvale, CA

1d•Onsite

About The Position

The Sr. Simulation & Synthetic Data Engineer will build simulation environments and data pipelines that support Intuitive’s autonomous surgical capabilities. The role combines 3D simulation, machine learning, and robotics, focusing on virtual surgical environments and synthetic data used to train perception and policy models and improve sim-to-real transfer for robotic surgery. The engineer will develop simulation environments, generate and manage labeled synthetic datasets, model surgical physics and instrument behavior, and collaborate with ML engineers on task curricula, reward functions, and evaluation benchmarks. They will validate simulated behavior against benchtop phantoms and real systems, and create reusable tools, APIs, and documentation for the broader team. The position is fully onsite at the Sunnyvale campus and is listed as a day shift role.

Requirements

Strong software engineering skills, including writing maintainable, tested code in Python, C++, and/or C#.
Comfort with Linux, Git, Docker, and GPU workflows.
Hands-on experience building in a physics simulator, game engine, or rendering/VFX pipeline; robotics simulators, real-time engines, and offline graphics pipelines are relevant.
Experience producing data used to train an ML model, or working closely with that process, and understanding how data quality and distribution affect model behavior.
Working knowledge of 3D and physics fundamentals, including coordinate frames, rendering, rigid-body dynamics, and contact dynamics.
Bachelor’s degree in Computer Science, Computer Graphics, Robotics, Electrical or Mechanical Engineering, Physics, or a related technical field, or equivalent practical experience.
Experience in games, simulation, graphics, robotics, or data systems is relevant; the posting expresses interest in demonstrated work rather than a specific number of years.

Nice To Haves

Advanced degree (MS or PhD) in a related area.
Deformable-object simulation experience, such as FEM, position-based dynamics, or differentiable physics.
Experience with NVIDIA Isaac Sim/Lab.
Surgical robotics simulation experience, such as ORBIT-Surgical or dVRK-based environments.
Experience with sim-to-real techniques, including domain randomization, system identification, privileged learning, or residual policies.
Familiarity with vision-language or vision-language-action models and the role of simulation data in supporting them.
Procedural or generative asset creation experience, such as NeRFs, Gaussian Splatting, or diffusion models.
Distributed compute at scale, including Ray, Kubernetes, Slurm, or multi-GPU/multi-node systems.
Medical imaging or surgical video pipeline experience.
Publications or open-source work in simulation, graphics, robotics, or AI.
Experience with tools such as Unity, MuJoCo, PhysX FEM, SOFA, DiSECt, NVIDIA Warp/Newton, PBD, USD, Omniverse Replicator, Blender, Airflow, or CUDA.

Responsibilities

Design and build high-fidelity simulation environments for surgical tasks.
Generate scalable synthetic datasets, including photorealistic imagery, segmentation masks, depth, optical flow, and kinematic state; manage their quality, versioning, and delivery.
Implement domain randomization and procedural scene generation to maximize sim-to-real transfer.
Model deformable soft-tissue physics, tool-tissue contact, and instrument kinematics that match Intuitive platforms.
Partner with ML engineers to define task curricula, reward functions, and evaluation benchmarks.
Develop and refine sim-to-real transfer strategies and validate simulated behaviors against benchtop phantoms and real systems.
Build reusable tools, APIs, and documentation so the broader team can create new tasks without deep simulation expertise.