Lead Product Manager, Rad AI

• Bachelor’s degree in Computer Science, Engineering, Data Science, or equivalent practical experience
• 7+ years of product management experience, including 2+ years leading AI/ML or data-driven products in production environments
• Proven success building and launching AI features at scale
• Strong understanding of machine learning lifecycles, model evaluation, and human-in-the-loop systems
• Exceptional communicator who can translate complex AI concepts into clear product strategy, technical requirements, and compelling user value for both technical and business audiences
• Highly collaborative and comfortable working with ML, data, and engineering teams as well as clinicians and business stakeholders
• Customer-obsessed and driven by solving real clinical problems and improving human performance through intelligent systems
• Thrives in a fast-paced, high-growth environment with evolving priorities

Desirable

• As a Lead Product Manager, AI, you will define, steer, and execute the strategy, development, and launch of Rad AI’s core AI-powered product capabilities. Your work will span speech recognition, report creation, and impression generation. You will partner with the Research team to use proprietary datasets, deep learning models, and generative AI systems to drive measurable improvements in radiologist productivity and patient outcomes
• You’ll work closely with machine learning engineers, data scientists, product managers, designers, and clinical experts to define, ship, and scale AI experiences that reach thousands of clinicians daily. This is a high-impact role where product strategy meets deep technical understanding of AI systems, user trust, and real-world clinical deployment at scale
• Own the product roadmap for AI-driven product capabilities—including speech, impressions, and reporting—from ideation through launch and iteration
• Collaborate with ML and data science teams to translate model capabilities into intuitive, high-value user workflows
• Drive end-to-end productization of AI capabilities, including model evaluation, performance monitoring, safety, and explainability frameworks for clinical environments
• Partner with engineering and design to create human-centric user experiences that build trust in AI-generated outputs and integrate seamlessly into radiology workflows
• Define and track key success metrics, including accuracy, latency, adoption, and user satisfaction
• Work with customers and radiologists directly to understand clinical pain points and validate product hypotheses
• Shape company-wide AI strategy, defining how Rad AI leverages large-scale data and generative AI models across our product lines
• Collaborate cross-functionally with regulatory, privacy, and security teams to ensure AI systems meet healthcare compliance and safety standards