Senior ML Research Scientist

About Rad AI

At Rad AI, we’re on a mission to transform healthcare with artificial intelligence. Founded by a radiologist, our AI-driven solutions are revolutionizing radiology—saving time, reducing burnout, and improving patient care. With one of the largest proprietary radiology report datasets in the world, our AI has helped uncover hundreds of new cancer diagnoses and reduced error rates in tens of millions of radiology reports by nearly 50%.

Rad AI has secured over $140M in funding, including a recently oversubscribed Series C ($68M round) led by Transformation Capital, bringing our valuation to $528M. Our investors include Khosla Ventures, World Innovation Lab, Gradient Ventures, Cone Health Ventures, and others—all backing our mission to empower physicians with cutting‑edge AI.

Our latest advancements in generative AI are used by thousands of radiologists daily, supporting more than one-third of radiology groups and healthcare systems and nearly 50% of all medical imaging in the U.S. at partners including Cone Health, Jefferson Einstein Health, Geisinger, Guthrie Healthcare System, and Henry Ford Health.

Recognized as one of the most promising healthcare AI companies by CB Insights and Aunt Minnie, and ranked by Deloitte as the 19th fastest‑growing company in North America, we are building AI‑powered solutions that make a real impact. Most recently, Rad AI was named to CNBC’s Disruptor 50 list, highlighting the innovation and momentum behind our mission.

If you’re ready to shape the future of healthcare, we’d love to have you on our team!

• Own a multimodal ML work-stream from problem definition through experimentation, evaluation, deployment, and iteration.

• Translate clinical and product needs into clear ML objectives, data strategies, model approaches, and success criteria.

• Build and evaluate modern ML systems, including transformers, self‑supervised learning, weak supervision, detection, localization, and segmentation.

• Work with image, report, and other clinical data to develop systems that are useful in real radiology workflows.

• Design rigorous evaluations that go beyond aggregate offline metrics, including clinically meaningful operating points, robustness, calibration, and performance across relevant data slices.

• Partner with engineering to product ionize models, make practical system tradeoffs, and learn from performance after launch.

• Investigate failure modes such as laterality errors, poor image or report grounding, hallucination, dataset bias, domain shift, and workflow disruption.

• Communicate research findings and technical decisions clearly through design documents, experiment reviews, and presentations to technical and clinical partners.

• Contribute to the research roadmap by identifying promising approaches, sharing learnings, and helping the team decide what to pursue next.

• Mentor less experienced researchers and engineers through project collaboration, code and experiment reviews, and technical guidance.

• Strong applied experience in computer vision, NLP, or deep learning, with a track record of independently designing experiments, analyzing results, and turning findings into working systems.

• Experience owning substantial ML projects across the full lifecycle, from data and modeling through production delivery.

• Deep hands‑on ability in Python and PyTorch, with strong intuition for model architecture, data quality, experimentation, and evaluation.

• Experience with modern vision or multimodal techniques such as vision transformers, contrastive learning, masked image modeling, or weak supervision, etc.

• The judgment to connect model performance to real user and clinical outcomes, including knowing when a benchmark improvement is not enough.

• Strong…