Algorithm Developer II New College Grad- Master's (Santa Clara, CA)

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

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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 .

About the Role

We are seeking a highly motivated engineer to develop data-driven models and analytics solutions that improve equipment performance, process efficiency, and operational decision-making. This role combines domain expertise, data analysis, and machine learning to support digital twin and related initiatives.

Key Responsibilities

• Develop and train models for different types of equipment and processes.

• Analyze data to identify trends, anomalies, and improvement opportunities.

• Build predictive methodology and optimize models using statistical and machine learning techniques.

• Collaborate with cross-functional teams to define requirements and deploy solutions.

• Validate model performance and drive continuous improvement.

Requirements

• Proficiency in Python, SQL, with demonstrated experience in machine learning.

• Solid analytical and problem‑solving abilities.

• Knowledge and experience of digital twin, modeling and simulation is a plus.

• Understanding of statistical concepts. Knowledge of semiconductor equipment or manufacturing is a plus.

• Strong communication and collaboration skills.

• Master's degree in Engineering, Physics, Mathematics, Computer Science, or a related technical field.

Applications will be reviewed on a rolling basis. Please apply by November 30, 2026. Note: This position may close early based on application volume or candidate selection.

Additional Information

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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 View email address on aiapply.co, or by calling our HR Direct Help Line at View phone number on aiapply.co, 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.