Sr. Machine Learning Engineer
It started with a simple idea: what if surgery could be less invasive and recovery less painful? Nearly 30 years later, that question still fuels everything we do at Intuitive. As a global leader in robotic-assisted surgery and minimally invasive care, our technologies—like the da Vinci surgical system and Ion—have transformed how care is delivered for millions of patients worldwide.We’re a team of engineers, clinicians, and innovators united by one purpose: to make surgery smarter, safer, and more human. Every day, our work helps care teams perform with greater precision and patients recover faster, improving outcomes around the world.The problems we solve demand creativity, rigor, and collaboration. The work is challenging, but deeply meaningful—because every improvement we make has the potential to change a life.If you’re ready to contribute to something bigger than yourself and help transform the future of healthcare, you’ll find your purpose here. 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 real-time ultrasound (US) imaging- specifically endobronchial ultrasound (EBUS) and min-profile ultrasound probes – for the accurate detection, segmentation, and classification pulmonary structures. This position focuses heavily on processing temporal, high-resolution ultrasound video streams, mitigating inherent acoustic artifacts, and fusing ultrasound data with pre-operative imaging modalities to improve targeting confidence and clinical outcomes.Essential Duties Drive the full cycle of medical imaging analysis software development, developing algorithms from R&D concepts into robust, commercial medical device products.Design, prototype, and implement advanced computer vision and machine learning algorithms tailored for real-time processing of diverse ultrasound modalities, including EBUS and ultrasound probes.Develop specialized algorithms to handle the unique challenges of high-frequency ultrasound data, including speckle reduction, acoustic shadowing mitigation, and temporal tracking across video frames.Apply and fine-tune state-of-the-art architectures, including spatio-temporal Vision Transformers (ViTs), recurrent networks, nnU-Net, Graph Neural Network (GNN) and Diffusion models, adapting them for noisy, high-frame-rate clinical ultrasound 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...