Senior ML Research Scientist

About the company

Rad AI builds generative AI software for radiology that automates impression generation, integrates with voice-recognition and EMR workflows, reduces dictation workload, and automates incidental-finding tracking and follow-up.

About this role

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 AuntMinnie https://www.radai.com/news/auntminnie-recognizes-rad-ai-omni-reporting-as-2023s-best-new-radiology-software, and ranked by Deloitte https://www2.deloitte.com/us/en/pages/technology-media-and-telecommunications/articles/fast500-winners.html 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 https://www.cnbc.com/2025/06/10/2025-cnbc-disruptor-50-see-the-full-list-of-companies.html 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! WHAT YOU’LL DO - 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 productionize 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. WHAT WE’RE LOOKING FOR - 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 collaboration skills across research, engineering, product, data, and clinical teams. - Clear written and verbal communication, including the ability to explain technical tradeoffs to both ML experts and clinical partners. - Typically 4+ years of relevant applied ML research or engineering experience, or equivalent scope and impact. We calibrate on demonstrated ownership rather than title or exact tenure. - An MS, PhD, or equivalent practical experience in Computer Science, Electrical Engineering, Machine Learning, Biomedical Engineering, or a related quantitative field. NICE TO HAVE - Experience with medical imaging, radiology, healthcare, or another high-stakes application area. - Familiarity with chest X-ray, CT, MRI, mammography, or other clinical imaging modalities. - Experience with DICOM, image-report pairing, medical data de-identification, radiology workflows, or clinically derived labels. - Experience evaluating models across patients, sites, scanner vendors, protocols, or other sources of distribution shift. - Familiarity with clinical validation, FDA or HIPAA considerations, or other regulated and privacy-sensitive environments. - Experience with 3D vision, longitudinal imaging, report generation, or clinical decision support. - Publications, open-source contributions, or other evidence of research credibility. WHAT SUCCESS LOOKS LIKE You’ll own and advance a meaningful research track from ideation through production. You’ll establish a strong understanding of the clinical problem, build a credible data and evaluation strategy, deliver models that perform reliably in practice, and help the team learn from real-world use. You’ll also become a trusted technical partner to the researchers, engineers, product leaders, data teams, and clinicians working on the broader ML roadmap. Ov