Senior Technical Product Manager, Applied AI

Mattel•El Segundo, CA

48d•$168,000 - $210,000•Onsite

About The Position

Mattel Digital Studios (MDS) is a cross-functional division that leverages digital innovation to amplify Mattel’s iconic brands, expanding into digital gaming, interactive experiences, and emerging technologies. The MDS Tech organization serves as the technical foundation across this portfolio, building scalable systems, tools, and platforms that enable product teams to move quickly and deliver high-quality digital experiences. Working across multiple products and partners, the team focuses on unlocking the value of AI, data, and modern development frameworks to power personalization, content creation, and next-generation play experiences. The Applied AI function sits at the intersection of product innovation and technical execution—unlocking new capabilities across creative workflows, internal tooling, and development pipelines. As a Senior Technical Product Manager, Applied AI, you will define, build, and scale AI-powered tools, workflows, and decision-support systems across Mattel Digital Studios. You will be accountable for identifying friction in cross-functional workflows — process misalignment, communication overhead, unreliable data, and stretched team bandwidth — and for translating that friction into a prioritized portfolio of AI solutions that measurably move the business. You will manage a portfolio framed in three buckets — Quick Wins, Efficiency Gains, and Strategic Bets — with the strongest opportunities progressing from proof-of-concept into strategic pilot and broader MDS rollout. The scope spans workflow automation, internal tooling, creative and content-generation tools, and decision-support systems tied to real operational use cases. Unlike traditional product roles, this position requires hands-on technical execution. You will actively build prototypes, experiment with AI models and APIs, and develop working solutions; but success goes beyond prototyping — it is seeing opportunities through productization, rollout, stakeholder alignment, team enablement, and repeatability across disciplines. You will partner with MDS leadership and the Product, Engineering, Operations, and Creative teams, bringing external vendors where buying beats building. This role is critical in ensuring that AI efforts are grounded in real business and user value, avoiding both strategy without execution and technology without clear application. You will also help flag and manage adoption risk—data quality, model accuracy and reliability, brand and creative integrity, and appropriate guardrails—partnering with the Office of AI and other governance stakeholders as needed. This role reports to the MDS Technical Director and works cross-functionally across MDS.

Requirements

Bachelor’s degree in Computer Science, Engineering, Product Design, or related field (or equivalent experience). Masters in Computer Science or related field preferred.
5–8+ years of experience in product management, engineering, or technical roles, with a demonstrated track record of personally shipping internal tools, automations, and cross-functional operational improvements—not just authoring requirements.
Hands-on experience building AI workflows and automations using LLM APIs, multimodal models, agent frameworks, and orchestration tools (e.g., OpenAI, Claude, LangChain, Python-based pipelines, Retool, low-code automation, etc.).
Demonstrated ability to take an AI solution from prototype through productization and rollout — including stakeholder alignment, enablement, and measuring business impact post-deployment — not just define requirements.
Strong product and operator mindset, with the ability to diagnose workflow pain points and connect user needs and business goals to technical solutions.
Experience working cross-functionally with production, engineering, design, creative, and business stakeholders.
Strong problem-solving skills, with a bias toward action and rapid iteration.
Ability to operate in ambiguity and navigate emerging technology landscapes.
Strong communication and storytelling skills, with the ability to influence across technical and non-technical audiences.
Demonstrated curiosity and growth mindset, staying current with rapidly evolving AI capabilities—including awareness of responsible-use considerations.

Nice To Haves

Masters in Computer Science or related field preferred.

Responsibilities

Own the applied AI roadmap from pitch to production — with direction and buy-in from MDS leadership, balancing speed, experimentation, and long-term value creation.
Identify and surface use cases and friction—assessing feasibility and scalability, translating them into high-impact AI opportunities.
Manage the opportunity portfolio—graduating the strongest proofs-of-concept into strategic pilots and broader MDS rollout.
Translate business needs into AI-powered tools, workflow automations, and decision-support solutions, defining clear requirements, success metrics, adoption targets, and iteration plans.
Continuously explore emerging AI capabilities and evaluate their application for internal tools, workflows, and content pipelines; apply them where operational impact, not novelty, justifies the work.
Build prototypes, lightweight applications, automations, and decision-support tools. Work hands-on, using modern AI platforms — LLM APIs, multimodal models, agent frameworks, orchestration tools, and evaluation harnesses.
Rapidly test and iterate on new ideas with a focus on measurable operational impact.
Partner with engineering, platform, and external vendors to productize, harden, and scale successful prototypes—owning build-vs-buy judgment and seeing opportunities through to live deployment.
Act as a bridge between MDS leadership, Product, Engineering, Operations, and Creative teams to identify and unlock AI opportunities within internal workflows and pipelines.
Secure leadership buy-in, drive alignment across stakeholders on priorities, and translate ambition into crisp execution plans with owners, timelines, and measurable outcomes.
Enable teams through reusable tools, workflow patterns, playbooks, and training that make adoption repeatable across disciplines—not one-off wins.
Collaborate with external partners (e.g., OpenAI) to accelerate internal capability building, and spearhead vendor evaluation and integration.
Manage adoption risks—data quality, compliance, hallucinations, over-reliance, brand and creative integrity—setting guardrails with the Office of AI and consulting with legal and privacy teams.
Define success metrics per opportunity—productivity, quality, cost, effectiveness—and report portfolio impact on a regular cadence.