Principal Machine Learning Engineer Reconstruction Quantitative Imaging

You will build machine-learning components that improve reconstruction quality, speed, robustness, and quantitative accuracy. You will define training and evaluation pipelines, datasets, and metrics; productionize models with monitoring and safe fallbacks; develop hybrid physics-informed algorithms; and create tools for experimentation and algorithm verification.

Responsibilities

• Build ML components for medical image reconstruction
• Define training and evaluation pipelines, datasets, and metrics
• Productionize models with performant inference, reproducibility, monitoring, and safe fallbacks
• Develop hybrid algorithms using physics and learned priors
• Build tools for rapid experimentation and rigorous algorithm verification

Requirements

• Applied machine learning
• Signal processing, imaging, or an adjacent domain
• Research prototyping and production-quality system development
• Model evaluation, ablation, data leakage avoidance, and reproducibility
• Machine learning for physics-based or inverse problems