Director, AI Product Management

The group you’ll be a part of

The Office of the CTO is where innovation takes center stage. We inspire our global technical community to take on grand challenges, understand emerging trends, identify the critical inflections, and drive our sustainability, Environment, Social, and Governance (ESG) practices that will define the next generation of semiconductors and continued impact.  

The impact you’ll make

Lam Research is seeking a Director of Enterprise AI Product Management to shape and steer an outcome-driven AI product portfolio that delivers enterprise-wide value. Positioned at the intersection of business strategy, product leadership, applied AI, architecture, and ecosystem development, the role turns evolving enterprise needs into coordinated roadmaps, scalable AI products, reusable capabilities, and strategic partnerships.

The Director leads horizontally across business functions, partnering closely with Platform Technology, AI Labs, Enterprise AI Solutions, architecture, data, security, and program-management teams. The role aligns these groups’ long-term product and capability roadmaps with enterprise priorities, technology evolution, and measurable business outcomes. It also acts as the product filter between incoming demand and committed work, directing investment to the highest-value opportunities.

Why This Role Matters

This role is central to connecting Lam’s evolving enterprise needs with the product, platform, applied-AI, and partner capabilities required to deliver durable business value. The Director ensures that internal roadmaps converge around the right enterprise problems, that technology and ecosystem choices remain scalable and adaptable, and that successful ideas progress from discovery through adoption and value realization.

What you’ll do

AI Strategy, Discovery & Roadmapping

• Maintain an integrated Enterprise AI product and capability roadmap that links evolving enterprise needs to the long-term roadmaps of Platform Technology, AI Labs, Enterprise AI Solutions, and enabling functions.
• Work with EAI product managers, Platform Technology, AI Labs, and solution teams to identify high-value problems, emerging capability needs, and cross-enterprise opportunities rather than accepting solution requests at face value.
• Facilitate roadmap tradeoffs among business demand, platform capabilities, applied research, reusable patterns, technical dependencies, and ecosystem options.
• Consolidate common needs across functions into reusable business capabilities, distinguishing self-service, point solutions, repeatable business solutions, foundational platform capabilities, and higher-order enterprise solutions.

Portfolio Intake, Prioritization & Investment Decisions

• Own the product-management front end of the unified Enterprise AI intake process, turning requests into well-defined product opportunities and targeted projects.
•  Assess requests with a lightweight, transparent prioritization framework covering strategic fit, reach, impact, confidence, effort, compliance, dependencies, data readiness, skills, and build-versus-buy considerations.
• Recommend whether to accept, redirect, request more information on, defer, or decline work, with clear rationale and value framing.

What you'll do (continued)

Product Definition, Validation & Lifecycle Ownership
•  Create a reusable framework for product requirements, technical and non-functional acceptance criteria, business success criteria, adoption measures, and expected value before architecture and development begin.
•  Ensure production products have clear ownership, support, monitoring, enhancement, and lifecycle models; prevent prototypes from becoming unsupported production systems by default.
•  Own the enterprise product portfolio across value streams, evaluate its broader impact, and actively manage its lifecycle.

Dependency Readiness & Execution Partnership
•  Identify and assign material dependencies, including data availability and quality, access, security, infrastructure, integrations, and skills, before projects become committed work.
•  Protect delivery integrity by separating request-to-start lead time from committed execution time and revisiting schedule or scope when assumptions change materially.
•  Partner with EAI functional leaders to recognize trends early and establish long-term planning for pre-emptive risk management.

Value Realization, Adoption & Governance
•  Act as the product-management integrator across the Enterprise AI hub-and-spoke model, aligning business-unit champions, Platform Technology, AI Labs, Enterprise AI Solutions, architecture, and AI super-user or connector communities.
•  Track AI investments, programs, and delivered value across the enterprise.
•  Define and track product KPIs, OKRs, adoption measures, and value-realization metrics, such as cycle time, productivity, revenue, risk, quality, or strategic impact.
•  Drive adoption and workflow integration with EAI product managers, recognizing that business leaders remain accountable for AI-enabled organizational transformation.
•  Ensure products meet Responsible AI, data governance, confidentiality, security, access-control, and source-transparency requirements.

What you'll do (continued)

Product Practices, Ecosystem Strategy & Partnerships

• Establish common product-management practices, reusable playbooks, reference patterns, intake artifacts, roadmap mechanisms, and portfolio governance that scale across the enterprise.
•  Lead AI ecosystem strategy by translating capability gaps and roadmap needs into partner requirements, evaluation criteria, engagement models, and investment or collaboration theses.
• Build and maintain a differentiated network of technology providers, AI innovators, implementation partners, research relationships, and strategic ventures with Lam Capital and other internal stakeholders.
• Partner on build-versus-buy and vendor decisions while retaining internal accountability for product strategy, architecture alignment, intellectual property, acceptance, and long-term ownership.

Leadership & Capability Development

• Lead through influence across a highly matrixed global organization, creating clarity across business, product, technical, and delivery teams without relying on organizational authority.
• Mentor product managers and cross-functional teams in discovery, product thinking, experimentation, outcome-based delivery, and responsible AI use.
• Build stakeholder and champion networks that improve demand quality, reduce duplication, accelerate adoption, and surface enterprise-scale opportunities.
• Turn external ecosystem learning into actionable implications for roadmaps, experiments, investment decisions, and capability development.
• Define partner evaluation criteria, including domain fit, technical differentiation, interoperability, data and IP protection, enterprise scalability, delivery capability, supportability, and strategic alignment.
• Maintain enough technical depth to challenge solution and platform assumptions, understand architectural tradeoffs, assess technical maturity, and link advances in models, agents, data, simulation, and enterprise platforms to roadmap decisions.

Who we’re looking for

• 12+ years of experience in product development/services, platform strategy, applied technology, or demand-to-delivery operations, engineering workflows, operations, or service business models.
• Demonstrated leadership of complex, cross-functional portfolios and the ability to align product, platform, applied-research, and solution roadmaps across a matrixed enterprise.
• Strong technical fluency across modern AI/ML and enterprise technology, including foundation models, agentic systems, data and knowledge architectures, APIs and integration patterns, cloud or hybrid platforms, evaluation, security, and Responsible AI; able to engage architects and technical leaders, test assumptions, and translate technical choices into product and portfolio implications.
• Proven experience translating ambiguous enterprise problems and technology shifts into product strategy, capability roadmaps, prioritized investments, requirements, success measures, and scalable solutions.
• Experience with AI/ML-driven transformation in industrial, engineering, manufacturing, service, or enterprise contexts, including assessment of technical readiness, data dependencies, architectural fit, and lifecycle implications.
• Broad understanding of enterprise data lineage, data modeling and metadata management.
• Strong business-value orientation with experience making investment tradeoffs, managing dependencies, and working directly with senior executives, technical leaders, business leaders, and external partners.
• Experience building and managing external AI ecosystems, including technology scouting, partner segmentation, technical and commercial evaluation, proofs of value, strategic partnerships, venture collaboration, and build-versus-buy-versus-partner decisions.

Preferred qualifications

• Experience shaping AI platform, applied-research, or emerging-technology roadmaps and connecting them to enterprise product needs.
• Familiarity with AI governance, Responsible AI, enterprise data architectures, ontology or knowledge graphs, agentic AI, model evaluation, and reusable enterprise AI patterns.
• Demonstrated AI transformation with measurable business impact; MBA or advanced technical degree (MS or PhD) in engineering, computer science, data science, or a related field preferred.

Leadership Attributes

• Enterprise-first, outcome-oriented product leader who can connect strategy to execution.Comfortable rejecting, redirecting, or reshaping demand when it does not meet value, readiness, or strategic criteria.
• Trusted partner to senior business and technology leaders with strong judgment in ambiguous environments.
• Able to simplify process while maintaining rigor in requirements, dependencies, governance, and accountability.
• Balances near-term delivery with reusable capabilities, long-term platform and AI Labs roadmaps, enterprise architecture, and ecosystem leverage.
• Curious, pragmatic, and adaptable as AI technology and operating models evolve rapidly.

Our commitment

We believe it is important for every person to feel valued, included, and empowered to achieve their full potential. By bringing unique individuals and viewpoints together, we achieve extraordinary results.

Lam Research ("Lam" or the "Company") is an equal opportunity employer. Lam is committed to and reaffirms support of equal opportunity in employment and non-discrimination in employment policies, practices and procedures on the basis of race, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex (including pregnancy, childbirth and related medical conditions), gender, gender identity, gender expression, age, sexual orientation, or military and veteran status or any other category protected by applicable federal, state, or local laws. It is the Company's intention to comply with all applicable laws and regulations. Company policy prohibits unlawful discrimination against applicants or employees.

Lam offers a variety of work location models based on the needs of each role. Our hybrid roles combine the benefits of on-site collaboration with colleagues and the flexibility to work remotely and fall into two categories – On-site Flex and Virtual Flex. ‘On-site Flex’ you’ll work 3+ days per week on-site at a Lam or customer/supplier location, with the opportunity to work remotely for the balance of the week. ‘Virtual Flex’ you’ll work 1-2 days per week on-site at a Lam or customer/supplier location, and remotely the rest of the time.

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