Lead Quality Engineer AI Enablement

CREATIVITY IS OUR SUPERPOWER. It’s our heritage and it’s also our future. Because we don’t just make toys. We create innovative products and experiences that inspire fans, entertain audiences and develop children through play. Mattel is at its best when every member of our team feels respected, included, and heard—when everyone can show up as themselves and do their best work every day. We value and share an infinite range of ideas and voices that evolve and broaden our perspectives with a reach that extends into all our brands, partners, and suppliers.

The Team

The Global Quality & Safety AI Strategy & Enablement team is building the foundation for responsible, scalable Applied AI across Quality. The team partners with Quality Engineering, Product Development, Regulatory, Packaging, Manufacturing, GTO, the Office of AI, and external build partners to turn Quality process knowledge into practical, governed, AI-enabled solutions.

This work is focused on solving real Quality problems through rapid prototyping, scalable solution design, advanced analytics, and decision support. The team helps Quality move from fragmented pilots to reusable capabilities such as AI-ready knowledge, grounded retrieval, document build workflows, agentic decision support, predictive models, workflow automation, and validated tools with human review where required.

The Opportunity

Mattel is currently seeking a Quality Engineer, Applied AI Solutions & Enablement in East Aurora, NY. This role will help Global Quality & Safety solve complex business and Quality problems by translating expert knowledge, process pain points, and decision needs into working prototypes, build-ready requirements, scalable solution designs, validation plans, and governed handoff packages.

The role requires a hands-on solution builder who can move quickly from problem to prototype, prove value with stakeholders, and then partner with GTO, the Office of AI, data teams, and external vendors to scale the right solutions responsibly. The person does not need to be the final production engineer for every solution, but they must be comfortable shaping technical direction, testing outputs, maintaining vendor relationships, and ensuring prototypes can become supportable enterprise capabilities.

The initial focus will center on advancing the Quality organization through AI-enabled decision support, knowledge access, and workflow transformation. The role will also support broader Quality AI initiatives spanning AI readiness, scalable solution design, intelligent automation, predictive insights, and experimentation to help build reusable capabilities across Global Quality.

The ideal candidate can operate between business process, Quality Engineering, data, AI, technology delivery, executive communication, and partner management. They must be able to build clarity from ambiguity, create compelling presentations and demos, align internal teams, manage vendor inputs, and convert prototypes into plans that can scale across Global Quality.

What Your Impact Will Be

• Solve Quality and business problems by framing the issue, identifying the decision or workflow need, designing practical solution paths, and building or coordinating rapid prototypes that demonstrate value.
• Rapidly prototype AI-enabled tools, workflow automations, dashboards, document-generation experiences, agentic workflows, decision-support models, and knowledge-retrieval solutions using approved Mattel tools, data sources, and partner capabilities.
• Partner closely with GTO, the Office of AI, data teams, cybersecurity, architecture, and approved technology partners to define scalable solution paths, data access needs, integration requirements, support models, and production-readiness criteria.
• Build and maintain productive vendor relationships, including scoping pilot work, preparing requirements, evaluating demos, comparing solution options, tracking deliverables, and ensuring external partners understand Quality’s business needs and constraints.
• Create clear presentations, demos, business cases, roadmaps, and executive-ready narratives that explain the problem, prototype, value, risk, investment need, scaling path, and decision required.
• Partner with Quality SMEs to map core workflows, decision points, systems, artifacts, handoffs, exceptions, and approval gates across Quality and Safety processes.
• Convert expert process knowledge into AI-ready task context, knowledge context, execution context, instruction context, validation criteria, and human-in-the-loop requirements.
• Lead process discovery and solution enablement for priority use cases, with an initial emphasis on NPD workflows such as ICR, DSP, CR/FPR, tooling, engineering pilots, production pilots, PCD, PRD, hazard review, labeling, packaging, reliability, lab testing, and missing-parts risk.
• Translate Quality use cases into build-ready requirements for RAG, RAG+Build, agentic workflows, predictive models, decision support, workflow automation, and scalable platform capabilities.
• Define the data, system, and knowledge inputs required for Quality AI capabilities, including Veeva, Polarion, Windchill, Agile PLM, SharePoint, Teams, GCP/BigQuery, product data, lab/testing information, manufacturing information, consumer feedback, social signals, and other approved datasets.
• Support business tradeoff and impact modeling, including questions related to pricing or elasticity, cost increases, sustainability or packaging choices, consumer response, risk, ROI, and scale economics.
• Build and maintain process maps, source inventories, metadata and taxonomy requirements, prompt and agent specifications, workflow diagrams, golden question sets, test cases, SME review logs, readiness scorecards, and handoff packages.
• Define practical quality gates for AI-enabled workflows, including evidence requirements, citation expectations, answer-quality criteria, regression testing, exception handling, escalation r