Principal Machine Learning Engineer (Reconstruction / Quantitative Imaging)

Highlights

• Build ML components improving reconstruction quality, speed, and accuracy.
• Collaborate on hybrid algorithms blending physics with learned priors.
• Productionize models with monitoring, safe fallbacks, and verification tooling.

Ideal candidate

Applied ML engineer comfortable bridging research and production in imaging or physics-based domains, with strong evaluation discipline and a passion for robust, high-stakes systems.

Requirements

• Strong applied ML experience with comfort in 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
• Demonstrated track record applying ML to physics-based or inverse problems via shipped projects or publications
• Pragmatic production mindset with reproducible training/inference and safe deployment in high-stakes contexts
• Background in computational physics or scientific computing
• Experience with PINNs and Neural Operators for PDEs in ultrasound simulation and imaging
• Experience in Agentic-SciML is a plus
• Hands-on data curation from messy real-world sources, ground truth definition, and labeling/simulation pipelines
• Background in data assimilation combining observations with physics-based models