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Location
In office (London)
Seniority
Staff · 6+ years exp
Employment
Full-Time

Confirmed on the employer's own hiring board on Nov 3, 2026. First seen by Alion on Sep 2, 2026. Meta scores B on the Alion truth index.

Overview
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Profile match
Meta Platforms is an American technology company founded in 2004 as Facebook and renamed in 2021 to signal its investment in immersive computing. It operates the largest family of social and messaging applications in the world, comprising Facebook, Instagram, WhatsApp, Messenger and Threads, and earns almost all of its revenue from advertising sold against that audience. Headquartered in Menlo Park, California, the company also develops the open Llama language models and the Reality Labs division behind Quest headsets and Ray-Ban smart glasses.
Meta is seeking an experienced QA Engineering Lead to drive product quality strategy across our consumer and business product portfolio, including Instagram, Facebook, Meta AI, WhatsApp, Ads, and Privacy. In this role, you will define and execute quality programs that span multiple engineering organizations and geographies, ensuring the reliability, correctness, and performance of products used by billions of people worldwide. You will partner closely with engineering, product, and infrastructure leaders to embed quality practices at scale, influence high-impact decisions, and raise the bar on test engineering across the organization.

Responsibilities

Define and own the product quality strategy across one or more product organizations, setting direction for test planning, automation, and quality standards Lead cross-functional quality programs spanning multiple products, teams, and geographic locations, driving alignment across engineering and product stakeholders Establish the technical direction for test strategy on high-ambiguity, business-critical initiatives to ensure product correctness and reliability at launch Partner with engineering and infrastructure teams to design and scale automation frameworks that prevent regressions and support long-term product reliability Identify systemic quality gaps and drive organization-wide adoption of improved processes, tooling, and quality-driven engineering practices Define and track quality metrics that measure test effectiveness, coverage, and overall product health across the portfolio Assess non-functional quality dimensions including accessibility, internationalization, localization, performance, and stress testing in partnership with specialized teams Advise and influence engineering leaders and cross-functional partners on quality tradeoffs, risk mitigation, and launch readiness decisions Mentor and develop quality and test engineers, sharing best practices and elevating quality standards across the organization Leverage data and testing insights to inform product decisions and drive measurable improvements in quality outcomes

Qualifications

6+ years of quality engineering or test engineering experience across consumer or business software products Experience leading quality strategies and programs across multiple teams and geographic locations, including distributed and offshore teams Experience with QA methodologies including test planning, test design, execution, and non-functional testing such as performance and stress testing Experience implementing and maintaining test automation frameworks for mobile and web applications Experience coding in Python, Java, PHP, C/C++, or an equivalent programming language Experience working across globally distributed engineering teams spanning multiple time zones and product domains Experience integrating AI tools into quality workflows to improve test coverage, efficiency, or defect detection Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Experience driving organization-wide quality initiatives and influencing engineering organizations at large scale Experience communicating quality strategy and risk assessments to technical and non-technical stakeholders, including executive audiences Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies

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