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Requisition ID
199652
Date posted
07/30/2026
Work Location Model
On\-site Flex
Work Location
Fremont\-CA
Work Country
United StatesThe group you’ll be a part of
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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
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Lam Research is seeking a Director of AI Product Management to lead the conception, roadmap development, and prioritization of transformational AI solutions across Lam’s core businesses. This role sits at the intersection of business strategy, product leadership, and applied AI, working closely with internal business units, product groups, and corporate functions to translate strategic opportunities into high\-impact, scalable AI products.
The Director will own the end\-to\-end AI product lifecycle for one vertical—from opportunity identification and value framing through delivery, adoption, and value realization—ensuring AI initiatives are tightly aligned with Lam’s business priorities and long term AI architecture roadmap.
Why This Role Matters
This role is central to Lam’s ability to operationalize AI as a strategic differentiator, ensuring advanced analytics and ML solutions move beyond experimentation and are embedded into the company’s core products, operations, and decision\-making processes at scale.
What you’ll do
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AI Product Strategy \& Roadmapping
- Define and maintain the Functional AI product roadmap, aligned with Lam’s strategic priorities, business\-unit value statements and in sync with Enterprise AI roadmap.
- Identify, frame, and prioritize transformational AI use cases across either around Operations spanning manufacturing, supply chain, logistics and order management or product and services spanning hardware, software engineering and equipment performance at customer.
- Translate business challenges into clearly defined AI product requirements, success metrics, and adoption strategies.
Cross\-Functional Leadership \& Stakeholder Engagement
- Act as the primary interface between business units / stakeholders, internal engineering functions and corporate functions to ensure clarity of objectives and alignment on delivery.
- Partner with senior leaders to evaluate tradeoffs across AI investments, balancing near\-term business impact with long\-term platform capabilities.
- Lead executive\-level reviews of AI product portfolios, roadmaps, and value realization.
AI Product Lifecycle Management
- Own the full AI product lifecycle, including ideation, discovery, roadmap definition, delivery, launch, adoption, and continuous improvement.
- Ensure AI solutions are designed for scalability, interoperability, and operational robustness, leveraging enterprise AI platforms and MLOps capabilities.
- Drive change management and adoption strategies to embed AI into core business workflows.
Value Realization \& Governance
- Define and track KPIs, OKRs, and value realization metrics for AI initiatives.
- Establish consistent intake, prioritization, and governance mechanisms for AI product investments across the enterprise.
- Ensure alignment with Responsible AI, data governance, security, and compliance requirements.
People \& Capability Leadership
- Collaborate, mentor, and guide high performing AI engineering teams
- Partner with business and AI stakeholders to build expert networks, drive collaboration, and accelerate business impact.
- Establish best practices for AI product management, including product discovery, experimentation, and outcome\-based delivery.
- Collaborate with corporate AI Solution, Platform technology and AI Lab team to develop enterprise\-wide AI product management capabilities.
Who we’re looking for
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- 10\+ years of experience in either product development and services or demand to deliver end\-to\-end operations, with deep understanding of capital equipment development lifecycles, operational workflows, and business models.
- Demonstrated product management leadership experience, including ownership of complex, cross\-functional product portfolios.
- Proven experience leading strategic, transformational initiatives, particularly in digital transformation and ML/AI\-driven solutions.
- Strong domain expertise in applying AI/ML to real\-world industrial, engineering, manufacturing, or enterprise\-scale use cases.
- Experience working directly with senior executives and business leaders to shape strategy and prioritize investments.
Preferred qualifications
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- Familiarity with AI governance, Responsible AI frameworks, and enterprise data architectures.
- Delivered an AI transformation for their organization or company with a measurable business impact
- MBA or advanced technical degree (MS) in engineering, computer science, or a related field.
Leadership Attributes
- Strategic thinker with a strong business\-outcome orientation.
- Ability to operate effectively in highly matrixed, global organizations.
- Trusted partner to senior business and technology leaders.
- Comfortable navigating ambiguity while driving structure, clarity, and execution.
- Passion for leveraging AI to deliver durable competitive advantage.
Our commitment
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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.*
Salary
CA San Francisco Bay Area Salary Range for this position: $168,000\.00 \- $350,000\.00\.
The above salary range for this position is relevant to applicants that reside or work onsite in the California, San Francisco Bay Area only. Salary offers will depend on factors that include the location you work from, your level, education, training, specific skills, years of experience and comparison to other employees already in this role. Actual salary may vary from salary offered due to numerous factors including but not limited to unpaid time off, unpaid leave, company mandated shutdown, and other relevant factors.
Our Perks and Benefits
At Lam, our people make amazing things possible. That’s why we invest in you throughout the phases of your life with a comprehensive set of outstanding benefits.
Salary Context
This $168K-$350K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Lam Research, 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 in Demand for This Role
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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($259K) sits 21% above the category median. Disclosed range: $168K to $350K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Lam Research AI Hiring
Lam Research has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Livermore, CA, US, Fremont, CA, US. Compensation range: $311K - $350K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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