AI/ML Engineer

Who We Are

Applied Materials is the global leader in materials science and engineering solutions that are at the foundation of virtually every new semiconductor chip and advanced display in the world. The equipment that we create and service is essential to advancing AI and accelerating the commercialization of next-generation semiconductor chips. Join us and push the boundaries of materials science and engineering in a company at the foundation of the electronics industry. The work we do together advances the world’s technology.

What We Offer

Salary:
$184,000.00 - $253,000.00

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 .

Role Overview

Join Applied Materials AI COE team to build intelligent systems that solve complex problems in semiconductor manufacturing.

As an AI / Machine Learning Engineer, you’ll design, develop, and deploy production AI solutions across machine learning, deep learning, reinforcement learning, and AI agents. A key focus of this role is using reinforcement learning (RL) to optimize dynamic manufacturing decisions such as scheduling, dispatching, planning, and resource allocation.

You’ll work with simulation environments, operational data, and real-world feedback to build intelligent systems that can learn, adapt, and improve complex factory operations.

What You’ll Do

- Design, train, evaluate, and deploy machine learning and deep learning models.
- Develop reinforcement learning solutions for complex decision-making and optimization problems.
- Build RL environments, reward functions, policies, and evaluation frameworks.
- Train and optimize agents using simulation and operational data.
- Explore techniques including policy optimization, model-based RL, multi-agent systems, and hierarchical learning.
- Build scalable training, inference, monitoring, and retraining workflows.
- Partner with software engineers to integrate AI capabilities into production and customer-facing products.
- Contribute to MLOps, experiment tracking, model versioning, observability, and lifecycle management.
- Research and prototype emerging technologies including Generative AI, Agentic AI, LLMs, RAG, and multi-agent systems.
- Collaborate with data scientists, software engineers, cloud architects, and semiconductor domain experts.

What We’re Looking For

Required

- Strong foundation in machine learning and deep learning.
- Hands-on experience building and deploying production ML systems.
- Strong Python development skills.
- Experience with PyTorch and/or TensorFlow.
- Demonstrated experience developing reinforcement learning solutions.
- Strong understanding of RL concepts including environments, policies, rewards, value functions, and exploration strategies.
- Experience with modern RL methods such as PPO, SAC, DQN, Actor-Critic, or similar approaches.
- Experience training and evaluating RL agents in simulation environments.
- Ability to formulate complex optimization problems as reinforcement learning problems.
- Experience with Git, testing, code reviews, CI/CD, and production software development.

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Preferred

- Experience building AI agents or autonomous decision-making systems.
- Experience with discrete-event simulation or simulation-driven learning.
- Experience combining RL with optimization techniques.
- Knowledge of scheduling, dispatching, operations research, or industrial optimization.
- Experience with Azure, AWS, or Google Cloud, Docker, Kubernetes, or MLflow.
- Experience with Generative AI, LLMs, agent frameworks, or RAG.
- Semiconductor manufacturing experience is a plus.

Why This Role

Reinforcement learning is a core strategic capability for this team, not simply a research topic. You’ll help develop next-generation manufacturing optimization solutions and intelligent agents capable of learning and adapting to complex factory operations.

You’ll join an experienced, collaborative team of data scientists, AI engineers, software developers, and cloud architects working on challenging industrial AI problems with direct customer and business impact.

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Additional Information

Time Type:
Full time

Employee Type:
Assignee / Regular

Travel:
Yes, 10% of the Time

Relocation Eligible:
No

The salary offered to a selected candidate will be based on multiple factors including location, hire grade, job-related knowledge, skills, experience, and with consideration of internal equity of our current team members. In addition to a comprehensive benefits package, candidates may be eligible for other forms of compensation such as participation in a bonus and a stock award program, as applicable.

For all sales roles, the posted salary range is the Target Total Cash (TTC) range for the role, which is the sum of base salary and target bonus amount at 100% goal achievement.

Applied Materials is an Equal Opportunity Employer. Qualified applicants will receive consideration for employment without regard to race, color, national origin, citizenship, ancestry, religion, creed, sex, sexual orientation, gender identity, age, disability, veteran or military status, or any other basis prohibited by law.

In addition, Applied endeavors to make our careers site accessible to all users. If you would like to contact us regarding accessibility of our website or need assistance completing the application process, please contact us via e-mail at [email protected], or by calling our HR Direct Help Line at 877-612-7547, option 1, and following the prompts to speak to an HR Advisor. This contact is for accommodation requests only and cannot be used to inquire about the status of applications.