Robotics Engineering Intern

Intuitive•Sunnyvale, CA

About The Position

The Robotics Engineering Intern will contribute to developing a sim-to-real-to-sim platform for Intuitive’s surgical robot system. The work supports rapid prototyping and risk reduction for new controller architectures, particularly data-intensive semi-supervised and supervised autonomous systems that require extensive validation. The intern will develop tools to transfer and test controllers between simulation and robot hardware, build virtual models of the surgical field, and use feedback between simulation and reality to accelerate the development and evaluation of autonomous controllers. The internship is intended for a currently enrolled student who will return to an accredited degree program after the internship. The intern must be available to work approximately 40 hours per week for 10–12 weeks, starting in May or June; specific start dates are provided during recruiting. The position offers hourly pay, benefits, and a housing allowance. Compensation depends on relevant skills and experience, degree program, year in school, location, and internship duration. The role may require proof of vaccination against certain diseases, including COVID-19, depending on the role. Some roles may also be subject to U.S. export controls.

Requirements

Must be currently enrolled in and returning to an accredited degree-seeking academic program after the internship.
Must be available to work full-time, approximately 40 hours per week, for a 10–12 week period starting in May or June.
Currently enrolled in a Robotics, Electrical Engineering, Mechanical Engineering, Computer Science, or related degree-seeking program at the Master's or Doctorate level.

Nice To Haves

Experience with MATLAB and/or Python for control system design and analysis.
Experience with MuJoCo or similar constrained rigid-body dynamics simulators.
Experience with sim-to-real transfer methods for robotic systems.
Familiarity with NVIDIA Isaac Sim or similar platforms for realistic rendering and simulation.
Familiarity with embedded C++ for low-level controller implementation.
Awareness of video-based 3D reconstruction and environment synthesis techniques.
Awareness of autonomous or semi-autonomous control architectures.

Responsibilities

Implement novel control architectures in high-level targets such as MATLAB and low-level targets such as embedded C++.
Systematically test controller performance in simulation and on robot hardware.
Create simulation environments combining constrained rigid-body dynamics, such as MuJoCo, with realistic texture and lighting, such as NVIDIA Isaac Sim.
Synthesize simulated environments from video data, including geometry, texture, and inferred stiffness.
Perform closed-loop round-trip tests by training controllers in simulation, testing them experimentally, and using experimental data to construct new simulation environments.