Lead Technology Product Manager, AI Quality & Evaluation

Role overview Turn expert-defined quality standards and evaluation methods into product capabilities for AI-enabled healthcare experiences. The role focuses on the workflows, tools, automation, monitoring, and reporting that let teams assess quality before launch and continue improving products afterward; it does not set the underlying clinical standards or statistical methods.

Responsibilities - Translate quality standards and evaluation approaches into product requirements, workflows, and roadmap priorities. - Lead discovery and delivery for evaluation tools, automation, monitoring, evidence capture, and reporting. - Work with clinical, data, engineering, product, legal, compliance, and customer-facing teams to put evaluation into practice. - Define workflows for expert review, issue triage, launch readiness, ongoing monitoring, and quality reporting. - Find ways to scale evaluation while retaining suitable expert oversight. - Use evaluation results to guide improvements to product experience, content, model behavior, and operations. - Explain quality signals, evidence, and tradeoffs clearly to product leaders and customer-facing teams.

Requirements - At least five years of product management experience in healthcare, enterprise software, data products, platforms, quality, or a related field. - Strong product discovery and delivery skills, including requirements, roadmaps, experimentation, and metrics-based decisions. - Ability to turn complex clinical, technical, quality, or risk concepts into practical requirements and workflows. - Experience working across technical and nontechnical groups, such as engineering, data, clinical, compliance, operations, or customer teams. - Analytical skills for defining measures, dashboards, workflows, and decision criteria. - Clear communication about quality, trust, evidence, tradeoffs, and customer value.

Nice to have - Experience with AI-enabled products, healthcare technology, quality or content tooling, search quality, clinical validation, or decision support. - Familiarity with evaluation methods, benchmarks, monitoring, human review, automation, or applied machine learning. - Background in regulated settings or clinical workflows, or experience explaining product quality and responsible technology to customers.