Senior Director, AI Technical Programs

** Who We Are*
* Applied Materials is a global leader in materials engineering solutions used to produce virtually every new chip and advanced display in the world. We design, build and service cutting-edge equipment that helps our customers manufacture display and semiconductor chips - the brains of devices we use every day. As the foundation of the global electronics industry, Applied enables the exciting technologies that literally connect our world - like AI and IoT.

If you want to push the boundaries of materials science and engineering to create next generation technology, join us to deliver material innovation that changes the world.

** What We Offer*
* Salary:

$ - $

Location:

Santa Clara,CA

You'll benefit from a supportive work culture that encourages you to learn, develop, and grow your career as you take on challenges and drive innovative solutions for our customers. We empower our team to push the boundaries of what is possible-while learning every day in a supportive leading global company. Visit our Careers website to learn more.

At Applied Materials, we care about the health and wellbeing of our employees. We're committed to providing programs and support that encourage personal and professional growth and care for you at work, at home, or wherever you may go. Learn more about our benefits () .

** General Profile*
* This role leads the company's AI work that directly helps the  
** Process Support Engineer (PSE)
** community move faster and work more consistently. You'll set direction, keep the program moving, and make sure we're getting real results-better tools, better knowledge access, and better training-without creating risk around security, IP, or compliance. You'll work across Field teams, Business Units, Digital/IT, compliance partners, and learning/training groups, and you'll also lead a small team of AI practitioners who build and scale solutions.

A big part of the job is working with internal AI leadership teams and the BUs to adopt best practices, align roadmaps, and  
** pull together multiple separately-built capabilities into a smaller set of solutions that are truly optimized for PSE workflows** . And once something is ready, you make sure it actually lands in the field with  
** hands-on, PSE-relevant training**  (not just a slide deck and a link).

** Competencies*
* In plain terms, you're great at:

+  
** Running big technical programs end-to-end**  (scope, schedule, priorities, risks, budgets, and outcomes).

+  
** Working with lots of stakeholders
** and keeping people aligned when priorities compete.

+  
** Influencing without authority*
* -especially across BUs and centralized AI groups.

+  
** Thinking clearly with messy inputs
** and making good calls with incomplete information.

+  
** Explaining technical topics simply*
* -from leaders to working-level engineers.

+  
** Building practical AI solutions**  (GenAI/AI for engineering workflows), plus knowing how to handle data governance, security, Responsible AI, and model lifecycle realities.

+  
** Knowledge systems and retrieval*
* -how to structure information so people (and AI) can actually find and use it.

+ Enough  
** PSE workflow and integration awareness
** to understand what will (and won't) work in the field.

** Key Responsibilities*
* What you'll actually  _do_ :

** Build AI that helps PSEs every day.
** Focus on high-frequency work like finding the right info fast, summarizing technical content, supporting troubleshooting, automating repeatable workflows, generating reports, and improving structured documentation.  
** Use AI to build and maintain PSE knowledge bases.
** That includes making content "AI-ready," keeping it updated, and putting the right controls around it so the right people can access the right information.  
** Make knowledge easy to access.
** Deliver practical "chat/search/assistant" style access so PSEs can get answers faster, follow consistent guidance, and reuse validated best methods-without compromising classification, IP, or compliance.  
** Evaluate tech-inside and outside the company.
** Look at platforms, models, tools, vendors, and services. Run pilots, compare options, and recommend what actually improves PSE capability and productivity.  
** Own the roadmap and results.
** Define what success looks like, prioritize use cases, track progress, manage risks, and report outcomes in a way leaders can trust.  
** Lead a small team of AI practitioners.
** Help them prototype, build, and harden solutions-then integrate them with real data sources and existing systems so they work in practice, not just in a demo.  
** Show the work and listen hard.
** Run demos, evaluations, and beta programs with PSEs and leaders. Capture feedback and iterate until it's truly useful and scalable.  
** Train the PSE community to use AI well.
** Create hands-on training, playbooks, and working sessions that build practical skill and improve job velocity. Measure adoption and proficiency so we know what's working.  
** Translate real PSE pain into buildable…