Staff Machine Learning Engineer Save Saved Sunnyvale, CA, United States Engineering - Intuitive

Job Description We are developing next-generation AI and machine learning capabilities for the da Vinci robotic surgical platform. As a Staff Machine Learning Engineer, you will lead the design and development of perception and scene understanding systems that interpret surgical imagery in real time, enabling intelligent features that enhance surgeon awareness and decision-making during minimally invasive procedures. This role spans deep learning for surgical image understanding, probabilistic spatial modeling, and 3D scene reasoning. You will build ML systems that operate on endoscopic visual data and integrate multiple sources of clinical and anatomical information to support the surgical workflow. Your work will directly shape how our next-generation robotic systems leverage AI to improve the surgical experience and patient outcomes. We are looking for someone who is deeply product-oriented: you care about building systems that work reliably in the operating room, not just on a benchmark. You understand that the surgeon is your end user, and you are motivated by the clinical impact of helping them perform safer, more confident surgery on real patients. What You'll Do Design, train, and evaluate deep learning models for semantic understanding of surgical scenes, including dense segmentation and structure detection from endoscopic imagery. Develop structured modeling and inference approaches using statistical modeling, optimization, and related algorithmic methods for complex real-world data. Develop machine learning components and supporting algorithms that meet real-time performance constraints. Own the end-to-end model lifecycle from research prototype to production: architecture design, large-scale training, model optimization (ONNX, TensorRT, mixed-precision), and integration with the da Vinci C++ software stack. Define evaluation methodology with clinically meaningful metrics and statistical validation frameworks appropriate for medical device regulatory submissions. Collaborate with surgeons, clinical scientists, and human factors engineers to translate clinical needs into technical requirements. Partner with systems and software engineering teams to ensure ML components meet real-time latency, memory, and reliability requirements for deployment on embedded robotic platforms. Mentor junior engineers and research scientists; establish best practices for experiment tracking, model validation, and reproducible research. Contribute to intellectual property development through invention disclosures and patent filings.