Senior Machine Learning Engineer (Clinical Team)

Midjourney; San Francisco (CA); Hybrid; USD 140,000 - 190,000; Full time

Job summary

Midjourney is seeking an experienced ML engineer to own tissue-class segmentation and labeling models for ultrasound CT clinical analysis. You will design training pipelines, datasets, and evaluation metrics, while collaborating with clinicians and cloud teams to ensure robust, auditable model deployments.

Key tasks include retuning across 2D/3D inputs, defining dataset versioning, and ensuring compliant, low-latency inference with proper drift monitoring and fallbacks.

Qualifications

• Strong applied ML experience, architecting, training and evaluating models from scratch.
• Experience with image segmentation (2D/3D) and robust training-data choices.

Responsibilities

• Own tissue-class segmentation and labeling models and their training pipelines.
• Retune across 2D, 3D, and 2D×3D fusion as inputs update.
• Define pipelines, datasets, and metrics from ground up or open source.

Skills

Applied ML

Image segmentation

PyTorch

End-to-end model development

Design controls

Tools

MONAI

ITK / SimpleITK

3D Slicer

Job description

What you’ll do

1. Own the tissue-class segmentation and labeling models for the ultrasound CT clinical analysis layer, and the pipelines that make them trainable and verifiable.

2. Retune across 2D per-slice, 3D volumetric, and 2D×3D fusion as reconstructed image inputs are continuously updated, and clinical indications for use expand.

3. Define training/evaluation pipelines, datasets, and metrics from the ground up or from open source; map model behavior to user needs and design requirements.

4. Work with data labeling contractors, expert clinicians, and our internal cloud/data teams on labeling specs, QC, and dataset versioning.

5. 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.