# Senior Machine Learning Engineer (Clinical Team) — Midjourney > Source: > Published: 2026-09-18 00:17:33+00:00 # Senior Machine Learning Engineer (Clinical Team) ## At a glance - Salary - Not published - Location - San Francisco Bay Area Hybrid - Work type - Hybrid - Level - Senior - Posted - today - Verified live - today - Skills - PyTorch - Filed under - [Core ML](https://frontierroles.com/machine-learning-engineer-jobs/) ## How the pay compares This posting doesn't publish pay. 114 of the 143 [Senior Core ML roles in the United States](https://frontierroles.com/machine-learning-engineer-jobs/) on this board do: the middle half pay **$194k–$231k**, with a median of **$218k**. Middle half of the 114 that publish payMedian10th–90th percentileAnnual, USD [Apply on company site (opens in new tab)](https://jobs.ashbyhq.com/midjourney/9beaa654-e136-4e6d-b51b-4c5b3ccb7e2b/application) ## 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.