This role pays more than
68%
of similar roles. Most pay
$131,250–$207,000
— the shaded band above.
At the midpoint, this role pays about
$191k
versus about
$169k
for comparable roles.
Based on 240 similar postings.
Employer
About Applied Materials
Applied Materials is the world''s largest supplier of equipment, services, and software for the semiconductor and display industries, enabling the production of chips and advanced displays. Industry: Semiconductor Equipment
Applied Materials currently has
134 open roles
on FindRole.
Listed pay typically runs
$132,750–$182,500
across 134 roles with salary data.
Algorithm Developer III joins the Rocket team to advance rapid innovation methodologies within semiconductor manufacturing. This role operates at the intersection of artificial intelligence, machine learning, theoretical physics, and systems engineering to develop next-generation solutions for high-impact manufacturing applications. The developer will build scalable data pipelines to process high-volume sensor data, identify critical physics-based relationships, and create predictive and control models using a hybrid approach of first-principles modeling and physics-informed neural networks. Key responsibilities include developing ML surrogates, reduced order models, and robust software modules that integrate with real-world manufacturing tools. The role requires expertise in theoretical physics, applied mathematics, computational science, and machine learning. Candidates must possess strong software engineering skills to translate complex algorithms into production-ready code while solving technical problems related to sensor data analysis and automated control systems for semiconductor processes.
Design and develop scalable data pipelines to process high-volume sensor data from manufacturing systems.
Identify and model underlying physical relationships governing complex semiconductor manufacturing processes.
Develop predictive and control algorithms using a hybrid of physics-based methods and machine learning.
Build robust, maintainable software modules that integrate directly with real-world manufacturing tools.
Create ML surrogates or reduced order models based on rigorous physics simulations.
Optimize accuracy and performance tradeoffs to ensure rapid execution of algorithm modules.
Analyze large quantities of sensor data to iteratively improve simulation accuracy and model performance.
Implement models and code for user-facing applications, control systems, or dashboards.
What we're looking for
Ph.D. in Physics, Applied Mathematics, Computational Chemistry, Electrical Engineering, Mechanical Engineering, or a related quantitative discipline.
Master’s degree in a relevant quantitative field with at least 2 years of experience in industry or research.
Deep expertise in 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 skills to translate complex algorithms into clean, scalable, and production-ready code.
Demonstrated ability to develop high-quality, maintainable code through public repositories, open-source contributions, or peer-reviewed publications (preferred).
Experience with computational methods, computer-aided engineering, physical modeling, or analysis of complex systems (preferred).
Experience applying machine learning techniques to scientific, engineering, or industrial problems (preferred).