QA Engineering Lead (AI)

November 14, 2026

Lead

Menlo Park, United States

Description

The QA Engineering Lead (AI) sets technical quality direction and leads major quality initiatives for generative AI products. This hands-on role builds shared testing and evaluation capabilities, guides cross-team quality planning, and mentors engineers. The position focuses on reliable AI experiences across products and platforms.

Responsibilities

Define and drive quality strategy and roadmaps for major GenAI initiatives across teams
Perform hands-on testing of consumer and business applications, APIs, and AI models
Architect and implement shared AI evaluation and test automation systems
Establish evaluation methods, benchmarks, weighted scoring frameworks, and quality standards
Develop reliable evaluation signals by calibrating human and automated judgments
Analyze measurement uncertainty and investigate differences between offline and real-world outcomes
Identify systemic quality risks across models, data, applications, and infrastructure
Lead cross-team efforts to address quality risks and their root causes
Plan quality for launches and phased rollouts, aligning success criteria and dependencies
Define metrics linking model performance, product reliability, and user experience
Promote reusable evaluation frameworks and mentor engineers on quality practices

Requirements

Bachelor’s degree in a relevant technical field or equivalent practical experience
A required degree must be completed before joining Meta
At least 10 years of quality assurance, test engineering, or software engineering experience
At least three years testing or evaluating AI models
Experience leading a major technical quality initiative across multiple teams
Experience implementing scalable systems using Python, PHP, Java, C/C++, or equivalent
Experience resolving quality and reliability problems across client, backend, and distributed systems
Hands-on experience testing applications, APIs, and AI models
Hands-on experience with risk-based strategy, performance testing, and test automation
Experience evaluating quality metrics and using data for technical decisions
Experience leading quality engineering in fast-paced development teams
Experience setting technical direction and resolving ambiguous cross-team dependencies
Experience communicating technical problems and trade-offs to varied stakeholders
Experience mentoring engineers and promoting quality practices beyond one project
Experience leading evaluation strategy for generative text, image, voice, or multimodal products
Experience designing shared evaluation infrastructure and adopting it across engineering teams
Experience combining human review, model judgments, adversarial testing, and regression detection
Experience implementing responsible AI practices, including risk assessment and bias mitigation
Demonstrated improvements in reliability, evaluation coverage, regression prevention, or engineering efficiency
Demonstrated ability to use AI tools to improve workflows and measurable outcomes
Experience leading cross-platform quality efforts in large engineering organizations
Ongoing AI skill development and familiarity with emerging AI technologies
Experience with weighted scoring frameworks for evaluating frontier AI models
Experience with statistical evaluation, benchmark validity, and evaluator calibration
Experience connecting offline evaluation results with production quality
Experience partnering with AI research teams on scalable product quality practices