5-7 years of applied machine learning experience with a focus on model development and evaluation.
Proven expertise in image segmentation techniques across 2D and 3D environments.
Ability to navigate both research-oriented tasks and practical model deployment.
Proficiency in deep learning frameworks such as PyTorch.
Experience in design control environments with stringent documentation requirements.
Responsibilities
Manage tissue-class segmentation and labeling models for ultrasound CT analysis.
Adapt models across various imaging formats as clinical usage evolves.
Establish robust training and evaluation pipelines from scratch or utilizing open-source datasets.
Collaborate with external and internal teams for data labeling and quality control efforts.
Integrate models into a compliant, versioned analysis service ensuring reliability and monitoring.
Benefits
Collaborative work environment with expert clinicians and data scientists.
Opportunities to work on cutting-edge ML techniques in medical imaging.
Exposure to a variety of imaging challenges in healthcare.
Support in furthering your professional development through challenging projects.
Full Job Description
What you'll do
Own the tissue-class segmentation and labeling models for the ultrasound CT clinical analysis layer, and the pipelines that make them trainable and verifiable.
Retune across 2D per-slice, 3D volumetric, and 2D×3D fusion as reconstructed image inputs are continuously updated, and clinical indications for use expand.
Define training/evaluation pipelines, datasets, and metrics from the ground up or from open source; map model behavior to user needs and design requirements.
Work with data labeling contractors, expert clinicians, and our internal cloud/data teams on labeling specs, QC, and dataset versioning.
Help productionize models into a versioned, HIPAA-bound analysis service: reproducible/low-latency inference, per-prediction confidence, drift monitoring, and safe fallbacks.
What we're looking for
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.
Useful experience
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.
