Join the Paradox Product team at Workday to help reimagine recruitment and hiring with the first-of-its-kind conversational recruiting platform! The Paradox AI Assistant streamlines recruiting tasks from end-to-end, including screening, instantly scheduling interviews, and answering common candidate questions. The goal is to provide simple, frictionless chat, mobile, and text message-driven experiences that allow recruiting and hiring teams to spend more time with people and less time with software.
We’re looking for exceptional individuals to join our team and to partner in creating a set of consumer grade software experiences. We collaborate constantly and continually look for ways to work better together.
This team operate like an innovative startup within Workday: fast-moving, highly collaborative, and deeply technical, while still benefiting from Workday’s scale and stability. You’ll partner closely with product, engineering, data labeling, design, and go-to-market teams to ship consumer-grade, AI-first experiences that help recruiters and hiring teams spend more time with people and less time with software.
As an LLM Product Manager on the Paradox team, you will own the product lifecycle for core LLM-powered capabilities across candidate and recruiter experiences.
You’ll drive product discovery, define requirements, and partner with engineering and data science from design through launch, iteration, and ongoing optimization.
One key part of this role is hands-on AI evaluation. You will regularly review LLM outputs and AI conversation logs, perform data labeling, and dig into traces to identify gaps, edge cases, and failure modes. You’ll translate these insights into concrete changes to prompts, data, workflows, and system logic that measurably improve quality, reliability, and user experience.
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Own AI-driven features, including candidate and recruiter-facing experiences.
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Define product vision, goals, and roadmaps for your areas, balancing user value, technical feasibility, and business impact.
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Lead rigorous evaluation of LLM behavior: review outputs, label data, and partner with data science to refine prompts, metrics, and evaluation strategies.
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Collaborate closely with engineering to design scalable, robust solutions and iterate quickly on experiments.
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Synthesize input from customers, internal stakeholders, and the market into clear, prioritized product requirements.
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Communicate progress, tradeoffs, and learnings across a global, cross-functional set of partners, guiding teams toward high-impact outcomes.
This role is ideal for someone excited to be “in the weeds” with AI systems—understanding how they behave, why they fail, and what to adjust to make them meaningfully better for real users.