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Requisition ID
200885
Date posted
07/02/2026
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.
Shape the future of AI at enterprise scale while building it from the ground up as the launch‑stage Director of Lam’s Applied AI \& Innovation Lab.
Lam Research is establishing a new AI Lab in Fremont, CA to accelerate the creation of advanced, high\-impact AI solutions across the company. We are seeking a Director, AI Lab to lead the creation, operation, and long\-term evolution of this lab as a center of excellence for AI solution prototyping, experimentation, and applied innovation.
The AI Lab will function as a hands\-on innovation environment where internal teams—from product groups and engineering to operations and corporate functions—can rapidly develop, test, and mature AI\-driven solutions. This role is responsible for setting the technical vision, building the lab’s capabilities, and ensuring outputs transition effectively from prototype to production\-scale deployment.
Join us to lead the innovation as Lam's FIRST Director at our new AI Lab.
The impact you’ll make
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AI Lab Vision \& Strategy
- Define the mission, scope, and operating model of Lam’s AI Lab, aligned with enterprise AI strategy and business priorities.
- Establish the AI Lab as a destination for rapid prototyping, applied research, and solution acceleration, bridging experimentation and real\-world business impact.
- Identify priority AI technology themes and technical focus areas (e.g., ML, GenAI, vision, optimization, agents, automation) relevant to Lam’s businesses.
Technical Leadership \& Solution Development
- Lead hands\-on development of AI prototypes, proof\-of\-concepts, and experimental solutions across products, engineering, manufacturing, operations, and corporate functions.
- Guide technical teams in selecting and applying appropriate AI architectures, models, tools, and platforms.
- Ensure solutions are designed with scalability, robustness, and transition\-to\-production in mind, aligning with enterprise AI foundations and delivery standards.
What you’ll do
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Collaboration \& Engagement
Partner with internal business units, engineering teams, and corporate functions to identify high\-value AI opportunities and co\-develop solutions.
Provide an environment where domain experts can work side\-by\-side with AI engineers and data scientists to accelerate solution development.
Serve as a technical thought leader and advisor to senior leadership on advanced AI technologies and their practical application within Lam.
Operational Excellence
Build and manage the AI Lab’s operating processes, including project intake, prioritization, resourcing, and execution.
Establish best practices for experimentation, model validation, and knowledge sharing.
Ensure effective handoff of successful prototypes to product teams, engineering organizations, or AI product management for scaling and deployment.
Talent \& Culture Development
Recruit, develop, and lead a high\-performing team of AI engineers, researchers, and technical specialists.
Foster a culture of innovation, experimentation, and technical rigor within the AI Lab.
Help elevate AI literacy and applied AI capabilities across Lam through mentorship, demonstrations, and technical engagement.
Who we’re looking for
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- Required Qualifications
+ Deep expertise in AI and machine learning technologies, including hands\-on experience building and deploying advanced AI systems.
+ Proven experience leading technical AI teams in environments focused on innovation, prototyping, or advanced solution development.
+ Strong understanding of end\-to\-end AI solution development, from experimentation through production handoff.
+ Ability to communicate complex technical concepts effectively to both technical and business audiences.
+ Demonstrated leadership in fast\-moving, ambiguous environments.
Preferred qualifications
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Preferred Qualifications
Experience with enterprise\-scale AI platforms, data infrastructure, or MLOps\-enabled delivery models.
Familiarity with industrial, engineering, manufacturing, or enterprise workflows.
Experience in the semiconductor industry (capital equipment or process technology) is a strong plus, but not required.
Advanced degree (MS or PhD) in Computer Science, Engineering, AI/ML, or a related field.
Leadership Attributes
Builder mindset with a bias toward action and iteration.
Technically credible and hands\-on, with the ability to dive deep when needed.
Comfortable operating at the boundary between research, engineering, and business.
Collaborative, curious, and passionate about translating cutting\-edge AI into real business value.
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: $180K across 1841 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 3,708 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($259K) sits 18% above the category median. Disclosed range: $168K to $350K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Lam Research AI Hiring
Lam Research has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Fremont, CA, US. Compensation range: $350K - $350K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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