Sr. Machine Learning Engineer, Medical Imaging

Intuitive•Sunnyvale, CA

About The Position

The Ion™ endoluminal system is Intuitive's new robotic platform for minimally invasive biopsy in the peripheral lung, with an initial goal of improving the early diagnosis of lung cancer. We are seeking a senior algorithm engineer to play a lead technical role in conceptualizing, designing, and evaluating our next- generation planning and guidance software. This role is dedicated to developing advanced algorithms to analyze chest medical images for complex lung diseases. This role focuses heavily on the processing and fusion of diverse imaging modalities—including CT, CBCT, and X-ray images—to perform robust lung structure segmentation, anomaly detection, and longitudinal disease tracking, ultimately improving clinical outcomes. Drive the full cycle of medical imaging analysis software development, developing algorithms from R&D concepts into robust, commercial medical device products.

Requirements

MS or PhD in Computer Science, Electrical Engineering, Biomedical Engineering, or a related field.
5+ years of hands-on industrial experience in algorithm development, with a deep specialization in medical imaging and computer vision.
Exceptional proficiency in Python and C++, with a deep understanding of modern software component design for production environments.
Proven hands-on experience with ML frameworks and software toolkits, including PyTorch, OpenCV, MONAI, ITK, VTK, and model optimization tools such as TensorRT or ONNX.
Experience or knowledge of the development of AI/ML features in strict alignment with Good Machine Learning Practice (GMLP) principles, ensuring data representativeness and model transparency.
Excellent verbal and written communication skills for effective cross-functional collaboration with clinical and engineering teams.

Nice To Haves

A proven track record of shipping commercial AI/ML software products is bonus.

Responsibilities

Design, prototype, and implement advanced medical imaging and machine learning algorithms tailored for advanced lung structure segmentation, registration, respiratory anomaly detection, and bronchial analysis.
Apply and fine-tune state-of-the-art architectures, including nnU-Net, Vision Transformers (ViTs), DINO, Diffusion models, and Vision Language Models (VLMs), for clinical datasets.
Architect and execute model deployment pipelines, seamlessly integrating trained models into high-performance C++ production environments, utilizing techniques like quantization, pruning, and hardware acceleration to ensure real-time performance on constrained medical systems.
Support system integration and testing while generating formal design documentation and patent applications.