# Senior Machine Learning Engineer (Clinical Team) at Midjourney > Hybrid · United States · Senior · ML / AI · open. Source: https://alderhq.app/jobs/ashby%3Amidjourney%3A9beaa654-e136-4e6d-b51b-4c5b3ccb7e2b (read 2026-11-04). ## Facts (observed) - Company: [Midjourney](https://alderhq.app/companies/midjourney) - Location text: San Francisco Bay Area Hybrid - Where it can be done from (inferred): Hybrid · United States (evidence: "Location: San Francisco Bay Area Hybrid") - Posted: 2026-09-18 (47 days ago) - First seen by alder: 2026-11-02 · last verified: 2026-11-03 - Reposted: 0 times · edited: 0 times - Apply on the employer's site: https://jobs.ashbyhq.com/midjourney/9beaa654-e136-4e6d-b51b-4c5b3ccb7e2b - Skills listed (inferred): PyTorch ## Pay and sponsorship - Posted pay: not posted ## Description (the employer's text) ### 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. --- Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Amidjourney%3A9beaa654-e136-4e6d-b51b-4c5b3ccb7e2b and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.