Talent Technology Program Manager

Autodesk Autodesk · Enterprise · AMER - Canada - Ontario - Toronto - University Ave, Toronto, ON +1 · Remote

The Autodesk Talent organization is seeking a Technology Program Manager to drive the strategy, prioritization, and optimization of talent technology solutions that support hiring, development, and mobility outcomes—including a focus on accelerating AI-enabled capabilities. This role will partner closely with Enterprise Systems and People Insights & Solution Design teams to ensure systems, agents, and data are effectively leveraged to drive end-to-end Talent workflows, best-in-class candidate and employee experience, AI enablement, and business impact. The role requires experience applying AI/ML-enabled features or tools within enterprise platforms or workflows and a strong understanding of how data, workflows, and systems enable AI use cases.

What you'd actually do

  1. Own the holistic technology product portfolio that supports talent acquisition, talent development, and global mobility
  2. Define and manage the product roadmap and backlog, making prioritization trade-offs to maximize platform effectiveness, scalability, in partnership with Talent operations, Talent Development, ESE (systems) and PISD (data & insights)
  3. Drive full product lifecycle management from discovery and design through launch and continuous optimization ensuring return on technology investment
  4. Own Talent technology requirements, ensuring systems, integrations, and in-platform processes are scalable, well-architected, and aligned to operational needs
  5. Represent the voice of the user (candidates, employees, managers, Talent practitioners) to inform product decisions, technology-enabled workflow design, and experience improvements

Skills

Required

  • 8+ years of experience in HR/talent acquisition technology, program management, product management, or technology strategy, preferably within enterprise SaaS environments
  • Demonstrated experience driving technology-enabled business outcomes in a cross-functional environment
  • Strong ability to translate business needs into prioritized requirements and work effectively with technical and data partners
  • Experience working with Talent or HR technology platforms (e.g., LMS, HCM, performance management, or talent marketplaces)
  • Strong analytical and problem-solving skills, with the ability to leverage data and insights (in partnership with analytics teams) to inform decisions
  • Excellent stakeholder management and communication skills, with the ability to influence across functions and levels
  • Ability to operate independently, navigate ambiguity, and drive clarity in complex environments
  • Experience applying AI/ML-enabled features or tools within enterprise platforms or workflows (e.g., automation, recommendations, copilots)
  • Strong understanding of how data, workflows, and systems enable AI use cases, with the ability to translate business needs into AI-enabled product opportunities
  • Familiarity with responsible AI principles, including data privacy, bias mitigation, and appropriate use of employee data

Nice to have

  • Masters degree in Business, Human Resources, Information Systems, or a related field.
  • Experience working with enterprise HR systems (e.g., Workday, SAP SuccessFactors).
  • Familiarity with Agile methodologies and product management tools (e.g., Jira, Aha!, Productboard).
  • Experience in employee experience design or human-centered design practices.
  • Understanding of data governance, privacy, and compliance considerations in HR technology.
  • Experience working in global, matrixed organizations.
  • Experience identifying and scaling AI use cases within HR or Talent domains (e.g., hiring, skills inference, career pathing, workforce planning).
  • Familiarity with generative AI applications (e.g., copilots, conversational interfaces) and their use in improving employee and manager experiences.
  • Experience working with data science or analytics teams to operationalize AI/ML solutions within business workflows.
  • Understanding of enterprise AI governance frameworks, including model risk, data retention, and compliance considerations.
  • Exposure to vendor evaluation and selection for AI-enabled platforms or capabilities.
  • Global travel may be required.

What the JD emphasized

  • AI-enabled capabilities
  • responsible AI capabilities
  • AI/ML-enabled features
  • AI use cases
  • responsible AI principles
  • generative AI applications
  • AI-enabled platforms
  • enterprise AI governance frameworks