Product Engineer 2 - AI/Data Science at LAM

Setup: Hybrid
Level: not_specified
Posted: Aug 12, 2026

Summary from listing

Lam Research is hiring a Product Engineer specializing in Data Science within its Selective Etch Product Group. The role combines semiconductor process and hardware experimentation with data science, machine learning, statistical modeling, and advanced analytics to investigate process and equipment performance, support technology and product development, resolve customer escalations, and develop predictive models and analytics tools. The position is based in Fremont, California, and offers hybrid work arrangements.

Required Skills

Data Science
Machine Learning
Artificial Intelligence
Applied Statistics
Scientific Computing
Python
Root Cause Analysis
Predictive Modeling

Additional Information

Minimum Education: PhD or Master's degree in Materials Science, Mechanical Engineering, Electrical Engineering, Physics, Chemistry, Engineering Physics, or a related engineering or scientific discipline
Required Previous Experiences: Experience designing, executing, and interpreting laboratory, research, or engineering experiments
Experience applying statistical methods, machine learning, or AI techniques to scientific or engineering problems
Programming experience with Python and scientific computing, machine learning, and data analytics libraries
Preferred Qualifications: Semiconductor process, equipment, product engineering, or technology development experience
Experience with semiconductor plasma, etch, deposition, surface science, or related applications
Experience applying AI and machine learning techniques to physical systems, manufacturing processes, or scientific data
Knowledge of DOE, SPC, multivariate statistics, regression analysis, and predictive modeling
Experience developing predictive models, anomaly detection algorithms, or engineering analytics tools
Laboratory experience involving physics, chemistry, materials characterization, or semiconductor process development
Experience translating experimental observations into predictive models and actionable engineering recommendations
Familiarity with root cause analysis methodologies and failure analysis techniques
Strong organizational skills and ability to manage multiple technical projects simultaneously
Excellent collaboration skills in cross-functional and matrixed environments