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Midjourney

Senior Machine Learning Engineer (Clinical Team)

Midjourney San Francisco, California, United States

Research Services · 51-200 employees

8 h ago
Remote machine-learning Senior (5-10 yrs) Full-time United States
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About the role

You will own the development and maintenance of tissue-class segmentation models for ultrasound CT clinical analysis. This includes building training pipelines, ensuring model reproducibility, and collaborating with clinicians to productionize HIPAA-compliant analysis services.

What they look for

Machine Learning Image Segmentation PyTorch 3D Volumetric Imaging Ultrasound Imaging Data Pipeline Development Deep Learning Model Evaluation HIPAA Compliance Clinical Data Analysis Self-supervised Learning Active Learning MONAI ITK 3D Slicer Transformer Models

Requirements

The role requires strong applied machine learning experience with a focus on image segmentation and deep learning frameworks like PyTorch. Candidates should be comfortable working in regulated environments and possess the ability to bridge the gap between research iteration and production-grade model deployment.

Full 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.

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