Associate Manager, QA Strategy & Operations

About the Team The Performance Excellence (PE) team is redefining how DoorDash measures, governs, and improves quality at scale. We are building an AI-first, automated QA ecosystem that delivers high-precision insight across 100% of customer interactions —powering performance management, coaching, risk detection, and product feedback in near real time. After successfully scaling Automated QA (AQA) across Tier 1 Support, we are expanding into high-impact, higher-risk domains including Fraud, Integrity, Trust Safety, and in-house support. This work requires strong ownership of AI signal accuracy, refinement rigor, and end-to-end process design to ensure automation is trusted, fair, and operationally actionable. About the Role As Associate Manager, QA Strategy Operations , you will own large-scale, AI-led quality programs from design through production. This is a senior IC / early people-manager role focused on building and governing automated QA systems , not running traditional QA operations. You will lead cross-functional initiatives that improve the accuracy, stability, and adoption of AI-generated quality signals , partnering closely with Product, ML, Engineering, Operations, and Policy teams. Your work will directly influence how DoorDash defines quality, manages performance, and mitigates risk at scale. You’ll report to the Senior Manager, QA Strategy Operations within Performance Excellence (CXI) You’re excited about this role because you will… Own AI-led QA system expansion into complex domains (Customer Support, Fraud, Integrity, Risk, In-House), defining what is automatable vs. human-judgment required Design and operationalize quality signals at scale , including rubric logic, precision thresholds, false-positive controls, and rollout gates Drive signal accuracy and trust , partnering with ML and Calibration teams to improve precision, reduce FP/FN rates, and close dispute feedback loops Translate ambiguous quality problems into structured systems , from intake → signal design → calibration → launch → governance Influence the QA tech and model roadmap , prioritizing investments in automation, tooling, and infrastructure that improve signal reliability Establish durable process frameworks (intake, SLAs, readiness criteria, documentation) that allow AI quality programs to scale safely and repeatably Lead cross-functional execution across Product, Engineering, Ops, and Policy without formal authority—driving clarity, alignment, and delivery We’re excited about you because… You have 6–8+ years of experience in strategy, program management, operations, product ops, or quality systems—ideally in AI-enabled or data-driven environments You’ve worked on large-scale process or platform launches , where accuracy, governance, and change management mattered as much as speed You are comfortable operating at the intersection of AI models, business rules, and operational workflows You have a strong intuition for signal quality —understanding false positives, calibration tradeoffs, thresholds, and downstream impact You bring structured thinking and execution rigor , especially in ambiguous problem spaces with multiple stakeholders You can partner effectively with ML and Engineering teams , even if you are not a model builder yourself Hands-on experience with LLMs, prompt design, conversational analytics, or AI QA systems is a strong plus You care deeply about building systems that are accurate, fair, trusted, and scalable We expect this position to be filled by 5/25/26. Compensation The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future. In addition to base salary, the compensation for t ... (truncated, view full listing at source)