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About This Role
Company Description 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.
Job Description 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 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.
Qualifications What We’re Looking For:
- 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.
Experience, Education, & Skills:
- 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.
Additional Information
Don’t meet every single requirement? At Mattel, we are dedicated to an inclusive workplace and a culture of belonging. If you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we still encourage you to apply. You may be just the right candidate for this or other roles. Join Mattel's Talent Community and start receiving exciting updates about our company, our employees, and our culture. Also, interested in signing up for job alerts? Do so here!
How We Work:
We are a purpose driven company aiming to empower generations to explore the wonder of childhood and reach their full potential. We live up to our purpose employing the following behaviors:
- We collaborate: Being a part of Mattel means being part of one team with shared values and common goals. Every person counts and working closely together always brings better results. Partnership is our process and our collective capabilities is our superpower.
- We innovate: At Mattel we always aim to find new and better ways to create innovative products and experiences. No matter where you work in the organization, you can always make a difference and have real impact. We welcome new ideas and value new initiatives that challenge conventional thinking.
- We execute: We are a performance-driven company. We strive for excellence and are focused on pursuing best-in-class outcomes. We believe in accountability and ownership and know that our people are at their best when they are empowered to create and deliver results.
Our Approach to Flexible Work:
This role is an in-office role Monday-Thursday, and work from home on Fridays. We embrace a flexible work model designed to empower a culture of growth, optimism, and wellbeing, where every employee can reach their full potential. Combining purposeful in-person collaboration with flexibility, our focus is to optimize performance and drive connection for moments that matter.
Who We Are:
Mattel is a leading global toy and family entertainment company and owner of one of the most iconic brand portfolios in the world. We engage consumers and fans through our franchise brands, including Barbie, Hot Wheels, Fisher-Price, American Girl, Thomas & Friends, UNO, Masters of the Universe, Matchbox, Monster High, MEGA and Polly Pocket, as well as other popular properties that we own or license in partnership with global entertainment companies. Our offerings include toys, content, consumer products, digital and live experiences. Our products are sold in collaboration with the world’s leading retail and ecommerce companies. Since its founding in 1945, Mattel is proud to be a trusted partner in empowering generations to explore the wonder of childhood and reach their full potential.
Mattel’s award-winning workplace culture has been recognized by Forbes, Fast Company, Newsweek, Great Place to Work, TIME, and more.
Visit us at https://jobs.mattel.com/ and www.instagram.com/MattelCareers.
Beware of recruiting scams: Mattel will only contact you through official @mattel.com or @smartrecruiters.com email addresses and will never ask for payment or sensitive personal information during the hiring process.
Mattel is an Equal Opportunity Employer where we want you to bring your authentic self to work every day. We welcome all job seekers, and all applicants will receive consideration for employment without regard to race, ethnicity, color, national origin, religion, sex, gender, gender identity or expression, sexual orientation, veteran and protected veteran status, disability status, and or any other basis protected by applicable federal, state or local law.
Pursuant to the Los Angeles Fair Chance Ordinance and the California Fair Chance Act, qualified applicants with arrest or conviction records will be considered for employment.
The pay range for this position is between $105,000 and $120,000 annual. The pay range is indicative of projected hiring range, however base pay will be determined based on a candidate’s work location, skills and experience. Mattel offers competitive total pay programs, comprehensive benefits, and resources to help empower a culture where every employee can reach their full potential.
Videos to watch:
The Culture at Mattel
Corporate Philanthropy
Salary Context
This $105K-$120K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Mattel, Inc., this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($112K) sits 47% below the category median. Disclosed range: $105K to $120K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Mattel, Inc. AI Hiring
Mattel, Inc. has 2 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Positions span El Segundo, CA, East Aurora, NY, US. Compensation range: $120K - $120K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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