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Tualatin, OR

Internship

Hybrid

Intern

Lam Research is a leading supplier in the semiconductor equipment industry, dedicated to innovation in the design and engineering of etch and deposition products. The AI/ML Intern will collaborate with the modeling team to develop and implement machine learning models, assist in AI/ML solution design, and conduct data analysis to support model development.

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Responsibilities

Collaborate with the modeling team to develop and implement machine learning models and algorithms.
Assist in the design, development, and optimization of AI/ML solutions for various applications.
Conduct data analysis, preprocessing, and feature engineering to support model development.
Write and maintain high-quality code for AI/ML projects, ensuring efficiency and scalability.
Apply knowledge of computational solid mechanics or CFD to enhance AI/ML models and simulations.
Participate in research and development activities to explore new AI/ML techniques and methodologies.
Document project progress, methodologies, and results for internal and external reporting.

Qualification

Required

Pursuing a BS or MS degree in Computer Science, Engineering, Applied Mathematics, or a related field.
Experience working with and modifying/extending large codebases written by others.
Strong experience in AI/ML, including knowledge of machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
Proficiency in programming languages such as Python and Java.
Solid understanding of data structures and algorithms.
Supportive knowledge in computational solid mechanics or CFD is highly desirable.
Experience with data analysis and visualization tools (e.g., Pandas, NumPy, Matplotlib).
Excellent problem-solving skills and the ability to work independently and as part of a team.
Strong communication skills and the ability to present technical information clearly.
Ability to work full-time during summer.

Preferred

Experience with high-performance computing (HPC) and parallel processing.
Familiarity with cloud computing platforms (e.g., Azure).
Knowledge of version control systems (e.g., git) and collaborative development workflows.
Deep understanding of transport phenomena and the underlying mathematical principles.
Experience with object-oriented programming.
Solid understanding of statistical modeling/ data mining/active learning.