Lead Product Manager-(AI)
UKG · Lowell, MA · 1 mo ago
HybridMarketing$130k–$186k/yrFull-time
About the role
In this role, you will drive strategy, execution, and hands-on innovation within our Workforce Management Scheduling portfolio, with a particular focus on advancing Healthcare scheduling capabilities through AI.
Responsibilities
- Partner with product leadership to develop and execute a product strategy where AI is the core value driver, not a feature layer on top of an existing product
- Build rapid AI prototypes using tools like Cursor, Claude, Replit, or v0 to validate product direction and compress the time from insight to validated concept
- Define what "good" looks like for AI-powered scheduling outputs: model behavior, success criteria, edge cases, and the trust signals that matter to end users
- Partner with engineering on prompt design, model tradeoffs, and evaluation pipelines. Contributing technical judgment alongside product direction
- Build and maintain strong relationships with customers and partners to understand scheduling pain points and translate them into AI product opportunities
- Work closely with cross-functional teams to bring new AI-powered capabilities to market and enable Go-To-Market teams to tell a compelling story about what we've built
- Define and track AI-specific KPIs, not just adoption metrics, but output quality, scheduling accuracy, time-to-value, and user trust over time
- Conduct competitive intelligence with an AI lens: what are AI-native scheduling competitors building, where are they winning, and where can UKG's data advantage be decisive?
Qualifications
- 7+ years delivering or managing SaaS applications within an Agile environment, including demonstrated experience building or shipping AI-powered products
- Hands-on experience with AI in your product workflow: prototyping, defining model behavior, and measuring AI output quality, not just managing teams that do it
- Experience working with tools like Cursor, Replit, v0, Claude, or equivalent to prototype product ideas before handoff to engineering
- Technical fluency with AI systems: you understand prompt engineering, model tradeoffs, and evaluation basics at a reasoning level. You don't need to code it, but you need to think clearly about it
- Proven ability to evaluate where AI creates genuine product value versus where it's hype, and to make product bets based on that judgment
- Exceptional communicator and storyteller: can explain a model limitation to an engineer, frame an AI scheduling capability as a patient-care outcome to a hospital VP, and present a roadmap to a product VP. Adjusting depth and framing for each audience
- Proven ability to lead through influence and operate across teams without formal authority