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.
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.
Skills Required
- Strong applied machine learning experience developing, architecting, training, and evaluating models from scratch
- Experience with image segmentation, including semantic or instance segmentation in 2D and ideally 3D or volumetric data
- Ability to move between research iteration and producing quantifiable, testable models
- Fluency with modern deep-learning tooling such as PyTorch and current development practices
- Experience working under design controls with model documentation and verification requirements
- Experience with modern segmentation architectures and label generation, including U-Net, nnU-Net, 3D U-Net, transformer-based, or promptable segmentation
- Experience with self-supervised or semi-supervised learning, active learning, weak labels, or simulation-driven pretraining
- Hands-on ML data curation, ground-truth definition, labeling pipelines, or simulation pipelines using tools such as MONAI, ITK, SimpleITK, or 3D Slicer
- Experience with segmentation models for ultrasound imaging
- Experience with medical imaging or physics-based inverse problems involving CT, MRI, ultrasound, or related domains
- Experience deploying models in versioned, auditable, high-stakes settings
- Background in anatomy, medical imaging, body composition, or existing segmentation models
What We Do
Midjourney is an independent research lab exploring new mediums of thought and expanding the imaginative powers of the human species.
.png)
