Job description

About the Team Future Forward Research — Synthetic Data, builds the simulation and data infrastructure that powers Intuitive's autonomous surgical capabilities. The Role 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. What You'll Do • 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. What You Bring We care more about real depth in a few of these than shallow coverage of all of them. • 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. You've likely spent several years building games, simulation, graphics, robotics, or data systems — but we're far more interested in what you've built than in a specific number on your résumé. If you don't check every box, we still want to hear from you. Strong candidates often come from autonomous driving, humanoid robotics, gaming, or VFX rather than surgical robotics specifically. Education • Bachelor's degree in Computer Science, Computer Graphics, Robotics, Electrical or Mechanical Engineering, Physics, or a related technical field — or equivalent practical experience. • An advanced degree (MS or PhD) in a related area is a plus, not a requirement. We've hired strong engineers from all paths, including self-taught backgrounds and industry work in games, VFX, or robotics. Bonus Points None of these are required — they're signals, not gates. Any one of them is a nice plus. • 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 Tools You Might Use You won't touch all of these, and we don't expect you to walk in knowing them. Category Examples Simulation engines Unity, NVIDIA Isaac Sim/Lab, MuJoCo Deformable physics PhysX FEM, SOFA, DiSECt, NVIDIA Warp/Newton, PBD Rendering & data USD, Omniverse Replicator, Blender Infrastructure Docker, Kubernetes, Ray, Airflow, CUDA What Success Looks Like in Your First Year • Simulation environments for several core surgical tasks are operational and integrated with the ML training pipeline. • Synthetic data delivers measurable model lift on targeted perception or policy metrics. • A quantitative sim-to-real correlation is established on benchtop phantoms. • Parallelized infrastructure reliably generates thousands of training episodes per hour with automated dataset delivery. • A reusable task-creation framework lets ML engineers define new surgical skills without deep simulation expertise. Shift • Day Workplace Type • Onsite - This job is fully onsite in the Sunnyvale campus. Due to the nature of our business and the role, please note that Intuitive and/or your customer(s) may require that you show current proof of vaccination against certain diseases including COVID-19. Details can vary by role. Intuitive is an Equal Opportunity Employer. We provide equal employment opportunities to all qualified applicants and employees, and prohibit discrimination and harassment of any type, without regard to race, sex, pregnancy, sexual orientation, gender identity, national origin, color, age, religion, protected veteran or disability status, genetic information or any other status protected under federal, state, or local applicable laws. Mandatory Notices U.S. Export Controls Disclaimer: In accordance with the U.S. Export Administration Regulations (15 CFR §743.13(b)), some roles at Intuitive Surgical may be subject to U.S. export controls for prospective employees who are nationals from countries currently on embargo or sanctions status. Certain information you provide as part of the application will be used for purposes of determining whether Intuitive Surgical will need to (i) obtain an export license from the U.S. Government on your behalf (note: the government’s licensing process can take 3 to 6+ months) or (ii) implement a Technology Control Plan (“TCP”) (note: typically adds 2 weeks to the hiring process). For any Intuitive role subject to export controls, final offers are contingent upon obtaining an approved export license and/or an executed TCP prior to the prospective employee’s start date, which may or may not be flexible, and within a timeframe that does not unreasonably impede the hiring need. If applicable, candidates will be notified and instructed on any requirements for these purposes. We will consider for employment qualified applicants with arrest and conviction records in accordance with fair chance laws. Preference will be given to qualified candidates who do not reside, or plan to reside, in Alabama, Arkansas, Delaware, Florida, Indiana, Iowa, Louisiana, Maryland, Mississippi, Missouri, Oklahoma, Pennsylvania, South Carolina, or Tennessee. This position may be filled at a different job level than listed here depending on business need and/or on the selected candidate’s experience, knowledge and skills. Compensation will be based primarily on the job level at which the role is filled and the candidate’s qualifications, consistent with applicable law. We provide market-competitive compensation packages, inclusive of base pay, incentives, benefits, and equity. It would not be typical for someone to be hired at the top end of range for the role, as actual pay will be determined based on several factors, including experience, skills, and qualifications. The target compensation ranges are listed.

Requirements

Must-haves

About the Team
Future Forward Research — Synthetic Data, builds the simulation and data infrastructure that powers Intuitive's autonomous surgical capabilities.
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.
We care more about real depth in a few of these than shallow coverage of all of them.
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.

Nice-to-haves

An advanced degree (MS or PhD) in a related area is a plus, not a requirement. We've hired strong engineers from all paths, including self-taught backgrounds and industry work in games, VFX, or robotics.
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
Tools You Might Use
You won't touch all of these, and we don't expect you to walk in knowing them.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

mlmachine learningpythondockerkubernetesai

Platform: smartrecruiters
Effort: medium

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