Algorithm Developer 3

## 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. ## 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. ## 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 ## 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.