Principal Machine Learning Engineer (Reconstruction / Quantitative Imaging)
Confirmed on the employer's own hiring board on Oct 18, 2026. First seen by Alion on Jun 30, 2026.
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).