About the Job
At Prenuvo, we are on a mission to flip the paradigm from reactive “sick-care” to proactive health care. Our award-winning whole body scan is fast (under 1 hour), safe (MRI has no ionizing radiation), and non-invasive (no contrast). Our unique integrated stack of optimized hardware, software, and increasingly AI, co...
We are looking for a Senior AI Applied Scientist II to independently own a model domain end-to-end within our AI Research org, from problem framing through validated, production-deployed models.
As Prenuvo’s AI org moves from task-specific models toward foundational work (meaning pre-trained backbones, multi-task heads, and the addition of clinical text and structured data), we need scientists who design experiments rather than only run them, diagnose failure modes on their own, and shape how the team appro...
Key Responsibilities
Lead development of foundation models and multimodal systems for whole-body MRI, advancing self-supervised and large-scale representation learning across imaging, radiology, lab, and longitudinal data.
Own experiment design and validation study scoping for your domain, diagnosing failure modes independently.
Drive the data quality, annotation criteria, and quality standards your models depend on, engaging directly with clinical framing for your domain.
Guide the strategic application of generative and agentic AI, including LLM adaptation and workflow orchestration, to accelerate research and clinical workflows.
Contribute modeling approaches that others adopt, and define and execute a research roadmap that advances Prenuvo's foundation-model capabilities.
Lead clinical review for your domain, partner across clinical, annotation, and ML Engineering, and raise the code-quality bar for the team.
Deliver production-ready models with documented validation, clinical sign-off, and performance benchmarks, owning the technical narrative for your domain.
Required Skills & Abilities
Strong MSc or PhD from a top-tier institution in CS, biomedical engineering, statistics, mathematics, or a related field.
A minimum of 4 academic or industry years of ML experience, with a strong publication or applied-research record including at least one first-author work.
Expert-level PyTorch and the ability to write and review production-quality research code.
Demonstrated ability to own a model domain end-to-end (architecting, training, validating, deploying to a real performance bar) and to design experiments and validation studies independently.
Deep fluency in self-supervised learning: masked image modeling, contrastive learning, JEPA-style methods, knowledge distillation, and large-scale representation learning.
Strong command of modern vision and multimodal architectures: Vision Transformers, multimodal transformers, foundation models, and vision-language models.
Experience in medical image analysis (segmentation, detection, classification, biomarker extraction, longitudinal modeling) and multimodal fusion of imaging with radiology, clinical, lab, and structured patient data.
Experience applying generative and agentic AI (LLM adaptation, workflow orchestration) to healthcare or research applications.
Strong collaboration skills across ML Engineering, Product, and Clinical Ops, with high standards for scientific rigor balanced against delivery speed.
Experience in body composition, vascular imaging, organ segmentation, or lesion detection
Familiarity with FDA 510(k) submission processes or predicate-based regulatory pathways
Publications in top journals and conferences such as ISMRM, NeurIPS, MICCAI, ICML, RSNA, etc.
Experience with interpretable AI and state-of-the-art AI methods
Prior work in a health tech or clinical AI environment
