New College Grad - Algorithm Developer III - PhD (Winter 2026 Start)

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:

$161,000.00 - $221,000.00

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 .

About Rocket

Rocket is a team within Applied dedicated to advancing and scaling rapid innovation methodologies across the organization. We focus on accelerating breakthrough technologies by combining deep scientific expertise, advanced computational methods, and practical engineering to solve complex challenges in semiconductor manufacturing.

Position Summary

We are seeking a highly motivated and intellectually curious Algorithm Developer to work at the intersection of artificial intelligence and machine learning (AI/ML), theoretical physics, and systems engineering . In this role, you will drive the development of next-generation solutions for high-impact semiconductor manufacturing applications.

The successful candidate will contribute to multiple disruptive design initiatives by developing automated systems capable of ingesting and processing data from thousands of sensors, identifying critical physics-based relationships, and building predictive and control models. These solutions will leverage a hybrid approach that combines first-principles physics modeling with advanced machine learning techniques, including physics-informed neural networks (PINNs), to optimize complex manufacturing processes in real time.

Applications will be reviewed on a rolling basis. Please apply by October 23rd, 2026. Note: This position may close early based on application volume or candidate selection.

Key Responsibilities

• Design and develop scalable data pipelines to collect, process, and analyze high-volume sensor data from semiconductor manufacturing systems.
• Identify and model underlying physical relationships governing complex process behavior.
• Develop predictive and control algorithms using a combination of traditional physics-based methods and machine learning approaches.
• Build robust, maintainable software modules that integrate directly with real-world manufacturing tools and systems.
• Collaborate with multidisciplinary teams spanning physics, engineering, software development, and data science to accelerate innovation and technology deployment.
• Contribute to the design and implementation of intelligent automation solutions for advanced semiconductor manufacturing processes.
• Develop ML surrogates or reduced order models, based on rigorous physics simulations, including research, design, development and implementation & proliferation accompanying in accordance with project budgets and time schedules.
• Optimize accuracy / performance tradeoffs, to retain achieve but speed execution of algorithm modules
• Analyze large quantities of sensor and metrology data, and iteratively improve simulation and models to improve accuracy
• Implement models and code for user facing applications, in control systems or dashboards
• Maintain a clean and consistent code base, that can be extended by team

Ideal Candidate Profile

The ideal candidate possesses a strong foundation in theoretical and computational disciplines, coupled with practical software development expertise. You are passionate about applying scientific principles to solve real-world engineering challenges and are equally comfortable moving between mathematical modeling, machine learning, and software implementation.

You should demonstrate:

• Deep expertise in one or more areas of theoretical physics, applied mathematics, computational science, or simulation-driven engineering.
• Working knowledge of modern machine learning, data science, and statistical modeling techniques.
• Strong software engineering capabilities, including the ability to translate complex algorithms into clean, scalable, and production-ready code.
• A practical mindset focused on delivering solutions that interact with and control physical systems in real-world environments.

Qualifications

Minimum Requirements

• Ph.D. in Physics, Applied Mathematics, Computational Chemistry, Electrical Engineering, Mechanical Engineering, or a related quantitative discipline, or
• Master’s degree in one of the above fields with a minimum of 2 years of relevant industry or research experience .

Preferred Qualifications

• Ideal candidates will be available to start full time during the window November 2026 - February 2027
• Demonstrated ability to develop high-quality, maintainable code, evidenced through public GitHub repositories, open-source contributions, peer-reviewed publications, or equivalent technical work.
• Experience with computational methods, computer-aided engineering (CAE), engineering simulation, physical modeling, or the analysis of complex systems, with a focus on practical and impactful outcomes.
• Experience applying machine learning techniques to scientific, engineering, or industrial problems.
• Strong interest in sensors, instrumentation, hardware systems, robotics, or other technologies that bridge software with physical-world applications.
• Proven ability to work effectively in interdisciplinary environments and solve ambiguous, complex technical problems.

Why Join Rocket?

Rocket offers a unique opportunity to work on cutting-edge challenges at the convergence of physics, AI, and semiconductor manufacturing. You will help develop transformative technologies that directly influence next-generation manufacturing systems while collaborating with a team dedicated to rapid innovation, scientific excellence, and real-world impact.

Additional Information

Time Type:

Full time

Employee Type:

New College Grad

Travel:

Yes, 10% of the Time

Relocation Eligible:

Yes

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.