Process Eng Intern — Dielectrics Productivity (On-site/Remote)

Lam Research Corporation; Tualatin (OR); Hybrid; USD 28,000 - 41,000; Full time

Benefits offered by this job

Hybrid on-site/remote options

Competitive benefits

Job summary

Lam Research is seeking a Summer 2027 Process Engineering Intern in the DFBU Productivity group in Tualatin, OR. You will run dielectric deposition experiments in lab and clean-room settings, applying model-based and ML techniques to predict and prevent productivity losses, and analyze tool data to identify improvement opportunities.

You will present your findings to DFBU leadership, with mentorship from experienced engineers and a defined project aligned to business needs.

Qualifications

• Must be an M.S. or Ph.D. student in Chemistry, Physics, Electrical Engineering, Chemical Engineering, Mechanical Engineering or Materials Science/Engineering with strong academic standing, returning to school after the internship.
• Strong foundational understanding of physical sciences (Thermodynamics, Physical chemistry)
• Strong communication skills to convey project summary and results

Responsibilities

• Run experiments on dielectric deposition tools in lab and clean-room environments.
• Apply model-based and machine learning approaches to predict and prevent productivity losses.
• Analyze tool and fleet data (uptime, downtime, chamber recovery) to identify top improvement opportunities.
• Support root-cause investigations and preventive parts-replacement plans to reduce downtime and wafer scrap.
• Present findings and recommendations to engineering and leadership in a short technical memo

Skills

Strong communication

Physical sciences knowledge

Clean-room work

Education

MS/PhD student in Chemistry, Physics, E.E., Chemical Eng., Mechanical Eng., or Materials Science

Tools

Python

JMP

MATLAB

Job description

Lam Research is seeking a Summer 2027 Process Engineering Intern in the DFBU Productivity group in Tualatin, OR. You will run dielectric deposition experiments in lab and clean-room settings, applying model-based and ML techniques to predict and prevent productivity losses, and analyze tool data to identify improvement opportunities.

You will present your findings to DFBU leadership, with mentorship from experienced engineers and a defined project aligned to business needs.