AI Product Manager

Workday Workday · Enterprise · Tel Aviv, Israel

AI Product Manager for a conversational recruiting platform, focusing on LLM-powered capabilities. Responsibilities include product discovery, defining requirements, and leading hands-on AI evaluation (reviewing LLM outputs, data labeling, analyzing logs) to improve quality and user experience. Requires experience with AI/ML products, LLMs, RAG, and evaluation methodologies.

What you'd actually do

  1. Own AI-driven features, including candidate and recruiter-facing experiences.
  2. Define product vision, goals, and roadmaps for your areas, balancing user value, technical feasibility, and business impact.
  3. Lead rigorous evaluation of LLM behavior: review outputs, label data, and partner with data science to refine prompts, metrics, and evaluation strategies.
  4. Collaborate closely with engineering to design scalable, robust solutions and iterate quickly on experiments.
  5. Synthesize input from customers, internal stakeholders, and the market into clear, prioritized product requirements.

Skills

Required

  • 4+ years of experience as a Product Manager, or in a highly technical role (e.g., Data Science, ML Engineering) with a clear path toward product.
  • Proven track record of building AI/ML-powered products.
  • Strong communication and collaboration skills, with the ability to bridge the gap between technical teams (Engineering, Data Science) to reason about tradeoffs, and non-technical stakeholders (CSM, Product, Business) to align on product vision.
  • Able to turn ambiguous problems and noisy data into clear decisions and priorities.
  • Strong understanding and hands-on experience with AI and large language models (LLMs), including RAG architectures and LLM evaluation methodologies.

Nice to have

  • Direct experience evaluating model performance: reviewing outputs, labeling data, and using those insights to inform product changes.
  • Experience with conversational products, AI assistants, or agentic workflows.
  • Prior experience in HR tech or talent acquisition workflows.
  • Experience in a fast-paced or 0→1 / early-stage product environment.
  • Ability to collaborate across time zones and functions, and to advocate for users using data, examples, and clear narratives.

What the JD emphasized

  • hands-on AI evaluation
  • in the weeds

Other signals

  • AI Product Manager
  • LLM-powered capabilities
  • conversational recruiting platform
  • AI Assistant streamlines recruiting tasks
  • hands-on AI evaluation
  • review LLM outputs and AI conversation logs
  • perform data labeling
  • dig into traces to identify gaps, edge cases, and failure modes
  • translate these insights into concrete changes to prompts, data, workflows, and system logic
  • measurably improve quality, reliability, and user experience
  • AI-driven features
  • rigorous evaluation of LLM behavior
  • refine prompts, metrics, and evaluation strategies
  • scalable, robust solutions
  • iterate quickly on experiments
  • AI and large language models (LLMs)
  • RAG architectures
  • LLM evaluation methodologies
  • evaluating model performance
  • conversational products
  • AI assistants
  • agentic workflows