Heading

Tualatin, OR

Internship

Hybrid

Intern

Lam Research is dedicated to excellence in the design and engineering of etch and deposition products for the semiconductor industry. They are seeking a Data Scientist Intern to join the Equipment Intelligence Team, focusing on developing analytics pipelines and machine learning models to enhance equipment performance and support data-driven product development.

Apply Now

Responsibilities

Development of analytics pipelines and platforms leveraging big‑data and machine‑learning techniques to support a global installed base of wafer fabrication equipment
Creation and training of deep learning and machine learning models for equipment performance characterization, anomaly detection, predictive maintenance, and optimization
Integration of empirical learning methods with physics‑based or first‑principles models
Exploration, cleaning, and analysis of complex, high‑volume equipment datasets
Supporting model validation, deployment workflows, and documentation of findings
Collaborate with Hardware, Process, and Software engineering teams to define data requirements and guide data‑driven product development
Communicate insights, results, and visualizations to internal engineering teams and, when appropriate, global customers
Participate in experiment planning with engineering teams and support interpretation of experiment results
Contribute to improving internal tooling, workflows, and automation

Qualification

Required

Currently enrolled in a Bachelor's or Master's program in Computer Science, Data Science, Electrical Engineering, Mechanical Engineering, Applied Physics, Materials Science, or a related quantitative field
Able to intern for at least 3 months preferrably 6-9 months if available
Strong analytical, quantitative, and problem‑solving skills
Ability to learn new tools, modeling techniques, and domain knowledge quickly
Ability to work independently on scoped tasks and collaborate effectively within multidisciplinary teams
Strong written and verbal communication skills

Preferred

Coursework or project experience in machine learning, deep learning, statistical learning, or data mining
Experience building models using modern ML/DL frameworks (e.g., PyTorch, TensorFlow, JAX, Scikit‑learn)
Familiarity with distributed compute environments (cloud platforms, Spark, Ray, or HPC systems)
Experience with Python for data science (NumPy, Pandas, Matplotlib, etc.)
Experience working with large datasets, time‑series data, or sensor/telemetry data
Familiarity with experiment design, model validation, or data pipelines
Interest in semiconductor manufacturing, advanced equipment, or applied physics
Proficiency in Python or similar high‑level languages
Understanding of core ML/DL concepts and ability to implement models from examples or academic references
Comfort working with modern development tools (Git, notebooks, VS Code, containerization, etc.)
Ability to present complex quantitative concepts clearly and visually
Curiosity and willingness to learn in a highly multidisciplinary environment