QA Engineering Lead (AI)

We’re looking for QA Engineering Leads to help shape the quality of our GenAI products at Meta. You enjoy solving complex quality challenges, building scalable testing and evaluation solutions, and partnering with engineering and research teams to ship products rapidly and with confidence. In this role, you will independently lead quality engineering for a product area, turn ambiguous requirements into measurable quality standards, and drive improvements across the teams that build and support it.

Responsibilities

Own the end-to-end quality strategy for a Gen AI product area, from early development and model evaluation through launch and ongoing product improvements
Perform hands-on testing of consumer-facing and business-facing applications, APIs, and AI models
Design, implement, and evolve AI model evaluation processes, including representative datasets, product-aligned weighted scoring frameworks, automated evaluations, human review, and regression coverage
Establish product-specific quality standards for model behavior, including correctness, relevance, safety, and robustness, in partnership with engineering, product, and research teams
Assess quality risks, identify coverage gaps, and drive resolution of issues across model behavior, client applications, server systems, and infrastructure
Plan and execute risk-based test strategies covering functional, integration, end-to-end, performance, stress, and cross-platform testing
Build and maintain reusable test automation and evaluation tooling, integrate quality checks into development workflows, and improve the reliability and speed of test feedback
Define quality metrics and use data to improve test effectiveness, testing efficiency, regression prevention, and the overall user experience
Lead cross-team quality projects, align stakeholders on priorities and dependencies, and provide evidence-based recommendations on release readiness
Investigate complex failures, identify root causes, and partner with engineering and infrastructure teams to prevent recurring defects
Mentor QA engineers in test design, automation, and AI evaluation, and contribute to a quality-driven engineering culture

Qualifications

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
Currently has, or is in the process of obtaining, a Bachelor’s degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience. A degree must be completed prior to joining Meta
7+ years of experience in quality assurance and test engineering for software products or services
2+ years of experience in AI model testing or evaluation, including developing evaluation methods and defining model quality standards
Experience independently leading quality engineering for a complex product or system, and delivering measurable quality improvements
Experience developing test automation or evaluation tooling in Python, PHP, Java, C/C++, or an equivalent programming language
Hands-on experience testing and debugging consumer-facing and business-facing web or mobile applications, APIs, and backend services, and evaluating AI models
Hands-on experience with test planning, test design and execution, performance and stress testing, and maintaining client and server test automation
Experience using metrics and failure analysis to prioritize quality improvements and inform release decisions
Experience coordinating work across engineering and cross-functional partners, resolving dependencies, and adapting priorities in a fast-moving development environment
Experience in communicating technical risks and trade-offs to stakeholders and mentoring other engineers Demonstrated improvements in product coverage, test signal quality, automation reliability, or testing efficiency within medium to large engineering teams
Experience in leveraging data and quality metrics to identify trends, prioritize coverage gaps, and drive product quality improvements
Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
Experience in building reusable testing or evaluation frameworks adopted by multiple engineering teams
Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
Experience combining human evaluation with automated model-based evaluation and validating the reliability of the evaluation results
Familiarity with frontier AI model evaluation practices, including weighted scoring frameworks for assessing model quality and performance
Experience evaluating generative text, image, voice, or multimodal models, including nondeterministic behavior and model regressions
Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
Experience partnering with AI research teams to translate model capabilities and limitations into product evaluation criteria