Lead Quality Engineer AI Enablement

Mattel•East Aurora, NY

2d•Hybrid

About The Position

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.

Requirements

Bachelor’s degree in Engineering, Quality, Data Analytics, Information Systems, Product Development, Business Process Management, or a related field; equivalent practical experience will be considered.
5+ years of experience in Quality, Product Development, Engineering, Compliance, Operations, Business Analysis, AI enablement, data/analytics, technology delivery, solution consulting, or process transformation.
Demonstrated ability to solve business problems, create working prototypes or proofs of concept, test value quickly, and translate successful pilots into scalable implementation plans.
Experience partnering with IT, digital, data, platform, architecture, cybersecurity, or enterprise technology teams to move prototypes toward governed, supportable solutions.
Experience managing or coordinating vendor work, including requirements, demos, pilots, deliverables, tradeoff evaluation, and relationship management.
Strong presentation and storytelling skills, including the ability to create executive-ready narratives, demos, business cases, roadmaps, and decision materials.
Practical understanding of AI-enabled solution patterns such as RAG, AI agents, workflow automation, human-in-the-loop review, model evaluation, and validation/testing of AI outputs.
Strong ability to learn complex business processes quickly and convert expert knowledge into structured workflows, requirements, decision logic, validation plans, and scalable solution concepts.
Experience working with cross-functional teams across Quality, Engineering, Product Development, Regulatory, Packaging, Manufacturing, IT, data/analytics, and/or external partners.
Ability to define source-of-truth requirements, metadata needs, taxonomy, access considerations, feedback loops, and governance controls for knowledge-heavy workflows.
Strong analytical and systems-thinking skills; able to connect process steps, data sources, decisions, controls, risks, business outcomes, and scaling constraints.
Comfort working with evolving tools and technologies while maintaining focus on business value, risk, governance, repeatability, and adoption.

Nice To Haves

Experience in toys, consumer products, manufacturing, product safety, regulatory compliance, product development, or Quality Engineering preferred.
Familiarity with New Product Development gates and deliverables such as ICR, DSP, CR/FPR, PCD, PRD, functional specs, hazard analysis, labeling forms, lab testing, engineering pilots, production pilots, and final production start preferred.
Familiarity with enterprise Quality/Product systems such as Veeva, Polarion, Windchill, Agile PLM, GCP/BigQuery, SharePoint, Teams, or similar systems preferred.
Experience building, specifying, or validating AI-enabled tools, agents, document-generation workflows, knowledge bases, workflow automations, dashboards, data apps, or analytics products.
Experience with process mapping, business architecture, product ownership, solution design, design thinking, process mining, knowledge management, ontology/taxonomy design, context graphs, knowledge graphs, or requirements analysis.
Experience defining acceptance criteria, testing plans, golden question sets, regression tests, quality gates, audit logs, or validation frameworks for digital or AI-enabled systems.
Experience working with consumer feedback, product reviews, service transcripts, social signals, manufacturing data, lab test data, or product quality datasets preferred.
Ability to work hands-on with prototypes, prompts, data extracts, workflow tools, vendor demos, and presentation materials while also creating executive-level recommendations.
Demonstrated a growth mindset by staying curious and continuously learning, embracing challenges, and improving themselves.

Responsibilities

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 rules, and release readiness.
Help prevent fragmented AI solutions by identifying repeatable patterns, shared data needs, reusable components, and opportunities to consolidate disconnected pilots into scalable platform capabilities.
Measure and communicate outcomes such as time saved, reduced rework, improved decision visibility, readiness progress, workflow reliability, risk reduction, adoption, and ROI.