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Requisition ID
202582
Date posted
08/11/2026
Work Location Model
On\-site Flex
Work Location
Livermore\-CA
Work Country
United StatesThe group you’ll be a part of
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The Global Operations Group brings information systems, facilities, supply chain, logistics, and high\-volume manufacturing together to drive the engine of our global business operations. We help Lam deliver industry\-leading solutions with speed and efficiency, while actively supporting the resilient and profitable growth of Lam's business.
The Global Operations AI team is a newly formed, high\-impact organization within Lam Research’s Global Operations business unit. The team is responsible for defining, continuously evolving, and deploying the AI/ML/Automation roadmap across Global Operations in close partnership with Lam’s Enterprise AI office. Our mission is to translate advanced AI and automation capabilities into scalable solutions that drive measurable business outcomes across Lam’s global operations footprint.
The impact you’ll make
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We are seeking an Operations Technical Program Manager (TPM) to serve as a key connector between Global Operations business stakeholders and AI/ML/Automation delivery teams. This role works closely with AI/ML/Automation TPMs and Change Management Leads to shape use cases, define value levers, and drive disciplined execution of deployment activities from concept through adoption and value realization.
What you’ll do
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Business Engagement \& Requirements Definition
- Partner with Global Operations functional leaders and process owners to identify, frame, and prioritize AI/ML/Automation opportunities.
- Facilitate structured discussions to gather business requirements, operational constraints, dependencies, and success metrics.
- Define value levers, expected outcomes, and KPIs in collaboration with business and finance stakeholders and AI/ML/Automation TPMs.
- Translate operational needs into clear problem statements and execution\-ready inputs for technical teams.
Program Execution \& Deployment Leadership
- Drive end\-to\-end execution for assigned initiatives, coordinating across business owners, AI/ML/Automation TPMs, technical teams, and Enterprise AI partners.
- Own deployment planning, milestone tracking, dependency management, and business readiness activities.
- Ensure alignment across process changes, user workflows, training needs, and change management plans.
- Track risks, issues, and decisions; proactively drive resolution to maintain delivery momentum.
Cross\-Functional Collaboration \& Value Realization
- Act as the primary business\-facing TPM for AI/ML/Automation initiatives within Global Operations.
- Partner closely with AI/ML/Automation TPMs to ensure solutions are operationally relevant, deployable, and embedded into day\-to\-day workflows.
- Monitor post\-deployment adoption, operational performance, and value realization against defined KPIs.
- Capture lessons learned, help standardize repeatable engagement and deployment patterns across Global Operations.
Who we’re looking for
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- Bachelor’s degree with 15\+ years of experience; or Master’s degree with 12\+ years’ experience; or a PhD with 8\+ years’ experience; or equivalent experience
- 8\-12\+ years of experience in Operations, Supply Chain, Manufacturing, Planning, Logistics, Procurement, and/or related enterprise functions.
- Proven experience leading complex, cross\-functional programs involving business process change and technology deployment.
- Strong facilitation and communication skills, with the ability to translate business needs into structured requirements and execution plans.
- Experience partnering with technical teams (AI/ML, data, software, automation) without requiring hands\-on development.
- Solid program management skills, including roadmap planning, risk management, stakeholder alignment, and executive communication.
- Ability to operate effectively in ambiguous, fast\-moving environments with competing priorities.
- Experience in semiconductor, advanced manufacturing, or complex global operations environments strongly preferred.
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: Min $146,000 \- Max $311,000\.
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 $146K-$311K 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($228K) sits 6% above the category median. Disclosed range: $146K to $311K.
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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