Staff Machine Learning Research Scientist, Rad AI

• 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

Desirable

• 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
• 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