AI Experience Policy Specialist

Uber Uber · Consumer · Phoenix, AZ · Community Operations

This role serves as a human-in-the-loop quality and safety gate for AI deployments, focusing on manual evaluations of customer and agent interactions with AI-powered support tools. The specialist will assess response accuracy, relevance, and customer experience, identify defects and gaps, and contribute to the continuous improvement of AI programs by providing actionable insights to product and engineering teams. The role requires expertise in policy operations and support policy frameworks, with a focus on ensuring AI outputs align with current policies and support standards.

What you'd actually do

  1. Execute rubric-based evaluations on real interactions between Uber’s users (earners, riders, merchants) and our GenAI support products.
  2. Assess the accuracy, relevance, and overall customer experience of the responses provided by AI.
  3. Identify defects, edge cases, and gaps in the support experience and document findings into relevant and actionable insights for cross functional teams to action.
  4. Support the calibration and continuous improvement of various AI programs as we continue to scale to a larger audience.
  5. Become an SME of multiple tools & programs to provide comprehensive evaluations of varying interaction types.

Skills

Required

  • Ability to distill complex technical processes into clear, usable content
  • Strong understanding of support operations, AI tools, and agent workflows
  • Strong expertise in policy operations or support policy frameworks
  • A proactive, solutions-oriented mindset with attention to operational detail
  • Strong technical aptitude and willingness to quickly learn and manage new internal tools

Nice to have

  • Experience with escalation workflows, SOP development, or policy training programs.
  • Experience coordinating cross-functional initiatives and working with project management tools like Jira.
  • Familiarity with AI-driven tooling or similar automated support environments.
  • Proven track record of improving communication flows in tech, support, or automation environments

What the JD emphasized

  • policy accuracy
  • knowledge base alignment
  • policy operations
  • support policy frameworks
  • AI-driven tooling