Machine Learning Engineer

Intuitive•San Francisco, CA

9h•$139,400 - $236,000•Onsite

About The Position

The AI Research group within Intuitive Surgical has an immediate opening in Sunnyvale, CA for a Machine Learning Engineer with focus on physical AI systems, robotics simulation environments and end-to-end ML pipelines, contributing to new technology development for next-generation robot-assisted surgery platforms.

Requirements

Doctoral degree in computer science, electrical and computer engineering, or Master's degree with minimum (5) years industry experience developing robotics and machine learning applications.
Strong background in ML infrastructure, including designing training pipelines, data orchestration, and deployment of RL models at scale
Proficiency in GPU optimizations for either inference or rendering
Proficiency in Python, with familairty in frameworks like PyTorch, TensorFlow, or RL libraries, and a proven ability to write clean, scalable, and efficient code
Ability to research, implement, and adapt cutting-edge techniques from academic and industry sources into practical, production-ready solutions for scalable RL in simulation
Strong hands-on experience with Python (proficiency), C/C++ (proficiency), shell scripting
Excellent communication skills both written and verbal.
Self-starter and able to work in a collaborative and results-oriented environment.
Able to view live and recorded surgical procedures.

Nice To Haves

Ability to travel domestically and internationally (5-15%)

Responsibilities

Design, implement, and optimize scalable Simulation and RL infrastructure for training surgical robots in simulated environments, leveraging distributed systems for parallel processing and high-throughput simulations
Optimize performance across the simulation stack, including distributed systems, Inference, and rendering, to ensure optimal usage of hardware resources and fast, efficient simulations
Support efforts to reduce the sim-to-real gap through domain randomization, noise modelling, and physics-based constraints.
Develop simulation workflows to produce synthetic datasets for AI model training and validation.
Designing large-scale data pipelines from multimodal robot sensor streams (vision, depth, proprioception, action logs)
Running structured experimentation across architectures, datasets, and training strategies for physical AI systems
Contribute to end-to-end learning pipelines from data collection → training → evaluation → real-world deployment
Work with AI/ML engineers to integrate simulation outputs into training pipelines, especially for physics-informed models.
Deliver high-quality, production-ready code in a dynamic and fast-paced environment
Contribute to building new clinical datasets and data pipelines.
Participate in integration of new ML/CV algorithms into existing and future robotic platforms.
Collaborate with users and clinical advisors to iterate prototype designs based on feedback and performance.