Sr. Simulation & Synthetic Data Engineer

Intuitive•Sunnyvale, CA

8d•$190,300 - $322,100•Onsite

About The Position

Future Forward Research — Synthetic Data, builds the simulation and data infrastructure that powers Intuitive's autonomous surgical capabilities. You'll build the virtual surgical worlds and the data pipelines that train our perception and policy models. Day to day, that means designing simulation environments, generating large volumes of labeled synthetic data, and working with ML engineers to close the sim-to-real gap for robotic surgery. It's a hands-on engineering role at the intersection of 3D simulation, machine learning, and robotics.

Requirements

Strong software engineering. You write maintainable, tested code in Python, C++ and/or C#, and you're comfortable in a Linux / Git / Docker / GPU workflow.
Hands-on simulation or graphics experience. You've built things in a physics simulator, game engine, or rendering/VFX pipeline — robotics simulators, real-time engines, and offline graphics pipelines all count.
A feel for data and models. You've produced data that trained an ML model (or worked closely alongside that), and you understand how data quality and distribution show up in model behavior.
Working knowledge of 3D and physics fundamentals — coordinate frames, rendering, rigid-body and contact dynamics — enough to reason about why a simulated scene does or doesn't look and behave correctly.
Bachelor's degree in Computer Science, Computer Graphics, Robotics, Electrical or Mechanical Engineering, Physics, or a related technical field — or equivalent practical experience.

Nice To Haves

An advanced degree (MS or PhD) in a related area is a plus, not a requirement.
Deformable-object simulation: FEM, position-based dynamics, or differentiable physics
NVIDIA Isaac Sim/Lab
Surgical robotics simulation: ORBIT-Surgical, dVRK-based environments
Sim-to-real techniques: domain randomization, system identification, privileged learning, residual policies
Vision-language or vision-language-action models, and how simulation data supports them
Procedural/generative asset creation: NeRFs, Gaussian Splatting, or diffusion models
Distributed compute at scale: Ray, Kubernetes, Slurm, multi-GPU/multi-node
Medical imaging or surgical video pipelines
Publications or open-source work in simulation, graphics, robotics, or AI

Responsibilities

Design and build high-fidelity simulation environments for surgical tasks.
Generate scalable synthetic datasets including photo realistic imagery, segmentation masks, depth, optical flow, and kinematic state — and own 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's 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 spin up new tasks without deep simulation expertise.