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).