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 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! Why Join Us? We're looking for a Staff Machine Learning Research Scientist to help define and drive Rad AI's next generation of applied research in NLP and clinical AI. We work across LLMs, retrieval, representation learning, speech and multimodal modeling, and we care as much about evaluation and reliability as we do about state-of-the-art results. You will have scope, ownership, and a direct line from research to product. You'll collaborate closely with clinicians, engineers, and product leaders to translate foundational research into production-scale systems that improve outcomes for doctors and patients alike. As we grow, you will help shape standards for model quality, safety, and observability, and contribute to strategic initiatives that include computer vision and vision-language work. What You'll Do: - Own end-to-end applied research: frame the problem, design experiments, ship to production, and monitor impact against real-world metrics. - Set technical direction across LLMs, retrieval, and multimodal; run ablations/error analysis that change product decisions. - Build evaluation that matters: link offline metrics to online outcomes; define thresholds, monitoring, and rollback. - Partner to deliver with engineering and product—and, when relevant, clinicians/domain experts—to align data, success criteria, and timelines. - Raise the bar by mentoring peers and codifying standards for reliability, safety, and documentation. - Improve the platform (data, training, serving, observability) to speed iteration and ensure reproducibility. - Explore new directions, with computer vision/vision-language work as a nice-to-have for future strategic initiatives. What We're Looking For: - MS or PhD (or equivalent research experience) in Computer Science, Electrical Engineering, Computational Linguistics, Biomedical Informatics, or related quantitative field. - 7+ years of applied ML research experience (or PhD + 5 years, or equivalent evidence of Staff-level impact). - Depth in one or more areas: LLMs and NLP, computer vision, speech, recommendation/ranking, retrieval, or multimodal modeling. - Strong experimental rigor: clear hypothesis framing, offline→online linkage, calibration and stratified analyses, ablations that influence decisions. - Proven ability to take models to production - Hands-on with modern tooling: PyTorch and common experiment/ops tools (for example MLflow, Databricks, Ray, or similar). - System thinking: can choose methods based on constraints, design for observability and rollback, and document decisions clearly. - Collaborative communicator who writes crisp design docs and explains complex ideas to non-specialists; comfortable mentoring peers. Preferred Qualifications - Health data familiarity, including EHR or imaging - Experience in one or more areas: clinical NLP or LLMs, computer vision, speech, retrieval or multimodal modeling. - Shipped, measured models in production with monitoring and clear rollback; external or multi-site validation is a plus. - Workflow integration with EHR, RIS, PACS, or reporting systems; PowerScribe or Dragon exposure helpful. - Strong evaluation practices: calibration, slice analysis, and ablations - Safety and governance in sensitive domains, including PHI handling and HIPAA or FDA-adjacent environments. - Technical mentorship and contributions to team research culture; publications or impactful open-source work. - Practical tooling: PyTorch plus modern ML ops tools such as MLflow, Databricks, Ray, or Triton. Why This Matters: Radiologists are the invisible backbone of modern medicine. Every diagnosis, every surgery, every treatment plan begins with their interpretations. Yet they're often overwhelmed by cognitive load, repetitive tasks, and administrative overhead. At Rad AI, we're using ML to change that—building intelligent systems that understand medical context, streamline documentation, and amplify human expertise. You've already seen how AI can transform healthcare. Now help us push it further. Join us in shaping how A