Principal Machine Learning Engineer (Reconstruction / Quantitative Imaging)

What you’ll do

• Partner with medical image reconstruction scientists / engineers to build ML components that improve reconstruction quality, speed, robustness, or quantitative accuracy.
• Define training/evaluation pipelines, datasets, and metrics that map to user needs and design requirements.
• Productionize models: inference performance, reproducibility, monitoring for drift/regressions, and safe fallbacks.
• Collaborate on hybrid algorithms, incorporating physics and learned priors, denoisers, learned regularizers, and quality estimation.
• 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).