Senior Machine Learning Engineer (Clinical Team)

Strong applied ML experience with a track record of developing new models — architecting, training, and evaluating from scratch as well as benchmarking against existing models.

Experience with image segmentation (semantic/instance, 2D and ideally 3D/volumetric) and the modeling and training-data choices that make it robust across diverse patient anatomy.

Comfortable moving fluidly between open-ended research iteration and producing quantifiable, testable models.

Fluent in modern deep-learning tooling (e.g., PyTorch) and current development practices.

Comfortable working under design controls, where model changes carry documentation and verification weight.

Image segmentation and label generation with modern architectures (U-Net / nnU-Net, 3D U-Net, transformer-based and promptable segmentation like SAM), including the geometry that ties voxel- and mesh-level predictions back to a coordinate frame.

Learning under limited or noisy supervision: self-supervised / semi-supervised methods (masked autoencoders, contrastive pretraining like DINO/SimCLR), active learning, weak labels, and simulation-driven pretraining.

Hands-on experience with data curation for ML: building datasets from messy, real-world sources, helping to define ground truth, and managing labeling or simulation pipelines (MONAI, ITK / SimpleITK, 3D Slicer).

Experience with segmentation models for ultrasound imaging, whether on synthetic or real images

ML for imaging or inverse problems in physics-based domains (CT, MRI, ultrasound, or adjacent), and comfort working alongside reconstruction/signal-processing teams.

Deploying models in versioned, auditable, high-stakes settings.

A background in anatomy, medical imaging, or body composition and prior work with existing segmentation models is a plus.