Staff Machine Learning Engineer

Intuitive; Sunnyvale (CA); On-site; USD 180,000 - 230,000; Full time

Job summary

Intuitive is seeking a Staff Machine Learning Engineer in Sunnyvale to lead the design of perception and scene understanding for the da Vinci platform. You will drive deep learning for surgical imagery in real time and integrate clinical information to support workflow.

You will own the model lifecycle from research to production, optimize for real-time constraints, and collaborate with surgeons and systems teams to meet regulatory and reliability requirements.

Qualifications

• Experience building ML models for real-time medical imaging.
• Experience in productionizing models for embedded platforms.
• Collaborative in cross-functional teams.

Responsibilities

• Design, train, and evaluate deep learning models for semantic understanding of surgical scenes.
• Develop probabilistic spatial modeling and 3D scene reasoning.
• Ensure real-time performance on embedded robotic platforms.
• Own end-to-end model lifecycle from research to production.
• Collaborate with surgeons and engineers to meet clinical requirements.
• Mentor junior engineers and researchers.
• Contribute to IP through invention disclosures.
• Define evaluation metrics for regulatory submissions.

Skills

Deep learning

Endoscopic imaging

Real-time inference

C++

ONNX/TensorRT

Model deployment

Leadership/mentoring

Tools

TensorRT

ONNX

Embedded systems

Job description

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.