{"id":1229359,"url":"https://alion.io/job/midjourney-senior-machine-learning-engineer-clinical-team","title":"Senior Machine Learning Engineer (Clinical Team)","company":{"id":264,"name":"Midjourney","domain":"midjourney.com","url":"https://alion.io/company/midjourney","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"C","score":66,"open_postings":14,"ghost_share":0.571,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-26T05:57:25Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United 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verifiable.\n\nRetune across 2D per-slice, 3D volumetric, and 2D×3D fusion as reconstructed image inputs are continuously updated, and clinical indications for use expand.\n\nDefine training/evaluation pipelines, datasets, and metrics from the ground up or from open source; map model behavior to user needs and design requirements.\n\nWork with data labeling contractors, expert clinicians, and our internal cloud/data teams on labeling specs, QC, and dataset versioning.\n\nHelp productionize models into a versioned, HIPAA-bound analysis service: reproducible/low-latency inference, per-prediction confidence, drift monitoring, and safe fallbacks.\n\nWhat we’re looking for\nStrong applied ML experience with a track record of developing new models - architecting, training, and evaluating from scratch as well as benchmarking against existing models.\n\nExperience 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.\n\nComfortable moving fluidly between open-ended research iteration and producing quantifiable, testable models.\n\nFluent in modern deep-learning tooling (e.g., PyTorch) and current development practices.\n\nComfortable working under design controls, where model changes carry documentation and verification weight.\n\nUseful experience\nImage 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.\n\nLearning 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.\n\nHands-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).\n\nExperience with segmentation models for ultrasound imaging, whether on synthetic or real images\n\nML for imaging or inverse problems in physics-based domains (CT, MRI, ultrasound, or adjacent), and comfort working alongside reconstruction/signal-processing teams.\n\nDeploying models in versioned, auditable, high-stakes settings.\n\nA background in anatomy, medical imaging, or body composition and prior work with existing segmentation models is a plus.","description_format":"text","description_chars":2646,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Generative Image","Foundation 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