Midjourney

WHAT YOU’LL DO 1. Partner with medical image reconstruction scientists / engineers to build ML components that improve reconstruction quality, speed, robustness, or quantitative accuracy. 2. Define training/evaluation pipelines, datasets, and metrics that map to user needs and design requirements. 3. Productionize models: inference performance, reproducibility, monitoring for drift/regressions, and safe fallbacks. 4. Collaborate on hybrid algorithms, incorporating physics and learned priors, denoisers, learned regularizers, and quality estimation. 5. Help build tooling for rapid experimentation as well as rigorous verification of algorithm changes. WHAT WE’RE LOOKING FOR - Strong applied ML experience plus comfort with signal processing / imaging or adjacent domains. - Ability to move fluidly between research prototypes and production-quality systems. - Strong evaluation discipline: metrics, ablations, data leakage avoidance, and reproducibility. - A demonstrated track record of applying ML to physics-based or inverse problems (i.e., shipped projects, a portfolio, or publications.) USEFUL EXPERIENCE - ML for imaging/inverse problems (or adjacent) with strong evaluation discipline and comfort with GPU performance constraints. - Pragmatic production mindset: reproducible training/inference, regression testing, and safe deployment in high-stakes contexts. - A background in computational physics or scientific computing. - Leverage ML-based methods such as PiNNs and Neural Operators to solve partial differential equations arising in ultrasound simulation and imaging. - Experience in Agentic-SciML is a plus. - Hands-on experience with data curation for ML: building datasets from messy, real-world sources, defining ground truth, and managing labeling or simulation pipelines. - Background in data assimilation: combining observations with physics-based models (Kalman filtering, variational methods, ensemble approaches, or learned variants).

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.