Lead Product Manager, AI Speech
Listed on 2026-03-01
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IT/Tech
AI Engineer, Machine Learning/ ML Engineer
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 Aunt Minnie, and ranked by Deloitte 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 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:
As a Lead Product Manager for AI Speech, you will define, steer, and execute the strategy, development, and launch of Rad AI’s most advanced, AI‑driven Speech features. Your team will 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‑powered speech 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.
What You’ll Be Doing:
Own the product roadmap for AI‑driven speech 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 end‑to‑end productization of AI Speech‑to‑Text, 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 speech 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 our product lines.
Collaborate cross‑functionally with regulatory, privacy, and security teams to ensure AI systems meet healthcare compliance and safety standards.
Who You Are:
Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field.
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‑powered features at scale, ideally within enterprise SaaS or healthcare technology.
Strong understanding of machine learning life cycles, 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‑obses…
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