Sr. Simulation & Synthetic Data Engineer

Job description

Job 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.

Job 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.