Rad AI
Staff ML Research Scientist
San Francisco · Staff+ · Full-time
Sponsorship not specifiedDetected 279 days ago
DatabricksMachine LearningPyTorchNLPComputer VisionLLMsMLOpsComplianceValuationElectrical EngineeringHIPAAEHR/EMRPatient CareFDA RegulatoryRadiologyResearchCollaborationMentoring
About the role
- 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.
- If you're ready to shape the future of healthcare, we'd love to have you on our team!
Responsibilities
- Own end-to-end applied research: frame the problem, design experiments, ship to production, and monitor impact against real-world metrics.
- Build evaluation that matters: link offline metrics to online outcomes
- Partner to deliver with engineering and product-and, when relevant, clinicians/domain experts-to align data, success criteria, and timelines.
- Build evaluation that matters: link offline metrics to online outcomes; define thresholds, monitoring, and rollback.
- Collaborative communicator who writes crisp design docs and explains complex ideas to non-specialists; comfortable mentoring peers.
- 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.
Requirements
- 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).
- Proven ability to take models to production
Nice to have
- 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.
Skills
- PyTorch and common experiment/ops tools (for example MLflow, Databricks, Ray, or similar).
Compensation
- Annual company-wide offsite
Benefits
- At Rad AI, we're on a mission to transform healthcare with artificial intelligence.
- 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.
- 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.
- 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.
- Explore new directions, with computer vision/vision-language work as a nice-to-have for future strategic initiatives.
- LLMs and NLP, computer vision, speech, recommendation/ranking, retrieval, or multimodal modeling.
Company info
- What We're Looking For:
- Founded by a radiologist, our AI-driven solutions are revolutionizing radiology-saving time, reducing burnout, and improving patient care.
- 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.
This listing is sourced directly from Rad AI's careers page and normalized into a canonical job model.