Staff Machine Learning Engineer

Intuitive•Sunnyvale, CA

About The Position

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.

Requirements

M.S. or Ph.D. in Computer Science, Electrical Engineering, Robotics, Biomedical Engineering, Applied Mathematics, or a closely related field, with graduate training involving computer vision, machine learning, probabilistic modeling, or 3D scene understanding.
5+ years of post-Ph.D. or 7+ years of post-M.S. industry or applied research experience developing and shipping ML systems, with demonstrated impact on production-grade products or platforms.
Deep expertise in deep learning for visual recognition, including semantic segmentation, object detection, and/or instance segmentation.
Strong familiarity with modern vision architectures.
Strong foundation in probabilistic modeling and Bayesian inference, including experience with one or more of: graphical models, nonlinear optimization, or MAP estimation for structured problems.
Proficiency in 3D geometry and spatial reasoning: coordinate frame transformations, rotation representations (SO(3), quaternions, axis-angle), rigid and similarity registrations, and camera projection models.
Hands-on experience with C++ development in a production context.
Ability to read and navigate large C++ codebases, build system prototypes, debug C++ components, and interface ML models with C++ software stacks. Expert-level C++ is not required, but working proficiency is essential.
Experience with model optimization and deployment for latency-sensitive applications: ONNX, TensorRT, quantization, mixed-precision inference, or equivalent embedded/edge deployment toolchains.
Expert-level Python and PyTorch (or equivalent deep learning framework).
Comfortable with NumPy, SciPy, and scientific computing at scale.
Strong software engineering fundamentals: version control, testing, code review, reproducible experiment management, and collaborative development in a multi-disciplinary team.
Track record of publications or patents in relevant areas (computer vision, medical image analysis, surgical data science, robotics perception, or probabilistic modeling).

Nice To Haves

Experience with medical image analysis, including familiarity with clinical imaging modalities and annotation pipelines.
Experience with statistical shape modeling or related approaches for modeling anatomical or biological variation.
Familiarity with 3D computer vision techniques such as depth estimation, 3D reconstruction, or geometric reasoning.
Background in numerical optimization methods and experience applying them to real-world engineering problems.
Experience developing ML systems within regulated environments (FDA, ISO 13485, IEC 62304), including design controls, verification and validation, and statistical testing plans for medical devices.

Responsibilities

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.