AI/ML Engineer I – Medical Image Computing

AI/ML Engineer I – Medical Image Computing Location: Remote (US) Employment Type: Full-Time About ProVoyance ProVoyance builds AI-driven medical imaging software that converts radiological scans into detailed 3D anatomical models for procedure planning and navigation. In 2021 we received FDA clearance for one of the first AI-powered preoperative planning systems in orthopedics, and our platform now supports a broader range of image-guided interventions. We license our digital surgery platform — automated segmentation, planning, and navigation — to medical device manufacturers worldwide, partnering with companies across the field internationally to integrate AI into their imaging and robotic surgical systems. The Role You’ll join our AI team to develop deep learning algorithms for image-guided interventions across the full range of radiological imaging — X-ray, CT, MRI and US. Working alongside AI scientists, software engineers, and clinicians, you’ll take algorithms from research through validation and into FDA-cleared products. This is a hands-on role with broad exposure to the full ML lifecycle in a startup environment. What You’ll Do Design, train, and evaluate deep learning models for segmentation, landmark detection, registration, and 3D anatomical modeling Develop AI supporting image-guided interventions including surgical planning, navigation, and tumor ablation Build image processing pipelines for DICOM/NIfTI volumetric data across X-ray, CT, and MRI Analyze large clinical imaging datasets and improve data quality Design reproducible experiments and evaluate performance with appropriate statistical methods Write production-quality, well-tested code and collaborate with engineering on integration Support V&V activities for regulated medical device development Required Qualifications Bachelor’s in CS, Biomedical/Electrical/Computer Engineering, Applied Math, Physics, or related 2+ years professional or graduate research experience in medical image analysis, computer vision, or deep learning (graduate research and internships count) Proficiency in Python, PyTorch, NumPy, Git Experience with at least one medical imaging library: MONAI, ITK/SimpleITK, VTK, or OpenCV Familiarity with DICOM/NIfTI and 3D volumetric images (CT, MRI, X-ray) Working knowledge of CNNs, U-Net, Vision Transformers, segmentation, and registration Comfortable in Linux environments Preferred Qualifications Master’s in a related field 3D deep learning; nnU-Net, MONAI, TorchIO, or TotalSegmentator Model optimization: CUDA, TensorRT, ONNX Docker and cloud (GCP/Vertex AI a plus given our current migration; AWS also relevant) Reproducible ML pipelines and experiment tracking (MLflow, W&B) Annotation tooling (3D Slicer, ITK-SNAP) Regulated medical device development; familiarity with IEC 62304, ISO 13485, or GMLP Open-source contributions or publications Why Join Your work ships in commercial, FDA-cleared devices used in real surgical workflows — not just papers. You’ll get end-to-end exposure from research through regulatory validation, work directly with leading device OEMs and clinicians, and have a highly visible impact at a fast-growing startup. Fully remote within the US. We value engineers comfortable wearing multiple hats — taking ownership across algorithm development, software engineering, and data analysis, and learning new tools quickly.