Associate Manager, Qa Strategy & Operations

DoorDash DoorDash · Consumer · New York, NY · 621 Support Management

This role focuses on building and governing AI-led automated QA systems for DoorDash, aiming to improve the accuracy, stability, and adoption of AI-generated quality signals across various domains like Customer Support, Fraud, and Trust & Safety. It involves designing and operationalizing quality signals, driving signal accuracy, and influencing the QA tech and model roadmap.

What you'd actually do

  1. Own AI-led QA system expansion into complex domains (Customer Support, Fraud, Integrity, Risk, In-House), defining what is automatable vs. human-judgment required
  2. Design and operationalize quality signals at scale, including rubric logic, precision thresholds, false-positive controls, and rollout gates
  3. Drive signal accuracy and trust, partnering with ML and Calibration teams to improve precision, reduce FP/FN rates, and close dispute feedback loops
  4. Translate ambiguous quality problems into structured systems, from intake → signal design → calibration → launch → governance
  5. Influence the QA tech and model roadmap, prioritizing investments in automation, tooling, and infrastructure that improve signal reliability

Skills

Required

  • 6–8+ years of experience in strategy, program management, operations, product ops, or quality systems
  • experience in AI-enabled or data-driven environments
  • large-scale process or platform launches
  • comfortable operating at the intersection of AI models, business rules, and operational workflows
  • strong intuition for signal quality
  • structured thinking and execution rigor
  • partner effectively with ML and Engineering teams

Nice to have

  • Hands-on experience with LLMs, prompt design, conversational analytics, or AI QA systems

What the JD emphasized

  • AI-first, automated QA ecosystem
  • high-impact, higher-risk domains
  • building and governing automated QA systems
  • accuracy, stability, and adoption of AI-generated quality signals
  • large-scale process or platform launches
  • AI models, business rules, and operational workflows
  • signal quality
  • structured thinking and execution rigor
  • Hands-on experience with LLMs, prompt design, conversational analytics, or AI QA systems

Other signals

  • AI-first, automated QA ecosystem
  • AI-led quality programs
  • accuracy, stability, and adoption of AI-generated quality signals
  • AI models, business rules, and operational workflows