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# Senior Machine Learning Engineer (Clinical Team)

- **Company:** MIDJOURNEY, INC.
- **Location:** United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Architectural Patterns, Machine Learning, Open Source Technology, Management of Software Versions, Pytorch, Convolutional Neural Networks, Machine Learning Operations, GPT
- **Published:** September 19, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=249c46d531328b91

## About the Role

* 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.

## Description

* 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.


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