Partner with imaging scientists to build ML components improving reconstruction.
Define training/evaluation pipelines, datasets, and metrics for user needs.
Productionize models: inference speed, reproducibility, drift monitoring.
Collaborate on hybrid physics-informed algorithms and quality estimation.
Build tooling for rapid experimentation and rigorous verification of changes.

🎯 Requirements

Strong ML experience with signal processing or imaging.
Able to move from research prototypes to production systems.
Strong evaluation discipline: metrics, ablations, reproducibility.
Track record applying ML to physics-based or inverse problems.
ML for imaging/inverse problems with GPU constraints.
Pragmatic production mindset: reproducible training/inference, safe deployment.

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