Reinforcement Learning Intern

• Ways of Working:
  Onsite - This job is fully onsite.
• Employee Type:
  Intern (Fixed Term)
• Global Job Level (HCM):
  Professional 3 (9)
• Min. Salary Region 1: 73 USD
• Min. Salary Region 2: 62 USD

It started with a simple idea: what if surgery could be less invasive and recovery less painful? Nearly 30 years later, that question still fuels everything we do at Intuitive
. As a global leader in robotic-assisted surgery and minimally invasive care
, our technologies—like the da Vinci surgical system and Ion
—have transformed how care is delivered for millions of patients worldwide.

We’re a team of engineers, clinicians, and innovators united by one purpose: to make surgery smarter, safer, and more human. Every day, our work helps care teams perform with greater precision and patients recover faster, improving outcomes around the world.

The problems we solve demand creativity, rigor, and collaboration. The work is challenging, but deeply meaningful—because every improvement we make has the potential to change a life.

If you’re ready to contribute to something bigger than yourself and help transform the future of healthcare
, you’ll find your purpose here.

Primary Function of Position

The Reinforcement Learning Intern will report to future forward research lead and contribute to the development and evaluation of reinforcement learning (RL) algorithms in support of supervised autonomy initiatives. The intern will identify, train, and test several RL algorithms using data collected from the Intuitive surgical robot systems. While the bulk of this work will take place on offline-collected data (i.e., not in real-time on the robot), a stretch goal would be to deploy an RL algorithm to control the robot in real-time.

• Collect tele-operated simulation and/or real data from the surgical robot while performing a realistic exercise (e.g., Fundamentals of Laparoscopic Surgery pick-and-place). This is referred to as trajectory data and is critical for RL training.
• Train off-the-shelf reinforcement learning algorithms (e.g., policy gradient methods, actor-critic methods, etc.) on the trajectory data and benchmark their performance using established metrics.
• Adapt reinforcement learning algorithms to the surgical domain and exercise of interest and identify techniques for improving performance relative to off-the-shelf RL models.
• Recommend a suggested algorithm / experimental setup moving forward (beyond the internship) based on experimental results, and present findings to the broader team.
• Deploy chosen RL algorithm either in simulation or on the dV5 to validate its performance in real-time (vs. purely offline evaluation).

Required Skills and Experience

University Hiring Program Eligibility Requirements:

• University Enrollment:
  Must be currently enrolled in and returning to an accredited degree-seeking academic program after the internship.
• Internship Work Period:
  Must be available to work full-time (approximately 40 hours per week) during a 10-12 week period starting May or June. Specific start dates are shared during the recruiting process.

Required Education and Training

• Current enrollment in an Engineering (e.g., Biomedical, Electrical), Robotics, Computer Science, or related degree-seeking program at the Doctorate level.

Working Conditions

• None.

Preferred Skills and Experience

Competitive applicants have one or more of these skills and a willingness to learn others.

• Deep experience with a deep learning framework (e.g., PyTorch or JAX).
• Strong familiarity with reinforcement learning algorithms (e.g., policy gradient methods, actor-critic methods).
• Willingness to learn about robotics and the application of RL to surgical robotics.
• Excel in communicating, both in written and verbal form,…