Principal Machine Learning Engineer (Reconstruction / Quantitative Imaging)

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Descripción

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 su

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