Responsibilities

• Own the product roadmap for AI-driven features and experiences, from ideation through launch and iteration.
• Collaborate with ML and data science teams to translate model capabilities into intuitive, high-value user workflows.
• Drive AI productization—including model evaluation, performance monitoring, safety, and explainability frameworks for clinical environments.
• Partner with engineering and design to create user experiences that build trust in AI 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.
• Contribute to company-wide AI strategy, helping shape how Rad AI leverages large-scale data and generative AI models across product lines.
• Collaborate cross-functionally with regulatory, privacy, and security teams to ensure AI systems meet healthcare compliance and safety standards.

Requirements

• Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field.
• 10+ years of product management experience, including 3+ years leading AI/ML or data-driven products in production environments.
• Proven success building and launching AI-powered features at scale, ideally within enterprise SaaS or healthcare technology.
• Strong understanding of machine learning lifecycles, model evaluation, and human-in-the-loop systems.
• Excellent communicator who can translate complex AI concepts into clear product strategy and user value.
• Highly collaborative and comfortable working with ML, data, and engineering teams as well as clinicians and business stakeholders.
• Customer-obsessed, driven by solving real clinical problems and improving human performance through intelligent systems.
• Thrives in a fast-paced, high-growth environment with evolving priorities.

Preferred Qualifications

• Experience deploying AI or ML solutions in healthcare or other regulated domains.
• Understanding of generative AI models (LLMs, multimodal systems) and their application to real-world workflows.
• Experience managing AI performance monitoring or MLOps systems in production.
• Previous product ownership across multi-tenant or enterprise-scale platforms.