Sr. Machine Learning Engineer - Intuitive

  • Znifa Technologies Private Limited
  • Sunnyvale, CA, United States
  • Aug 25, 2026
Full time Information Technology

Job Description

Job Description 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 while generating formal design documentation and patent applications.