Product Engineer 2 - AI/Data Science

Lam Research•Fremont, CA

13h•$86,000 - $183,000•Hybrid

About The Position

Join Lam as a Product Engineer with expertise in Data Science and Advanced Analytics, where you'll combine hands-on semiconductor experimentation with modern analytical methods to solve complex process and hardware challenges. In this role, you will work directly with process development, technology development, product engineering, and customer escalations. You will design and execute experiments, develop advanced test vehicles, analyze data from multiple sources, and apply statistical and machine learning techniques to identify root causes, optimize processes, and uncover underlying physical mechanisms. Operating at the intersection of semiconductor engineering, experimental science, and data analytics, you will transform laboratory and field data into actionable insights that improve product performance, accelerate technology development, and support data-driven engineering decisions.

Requirements

PhD or Master's degree in Materials Science, Mechanical Engineering, Electrical Engineering, Physics, Chemistry, Engineering Physics, or a related engineering or scientific discipline.
Demonstrated expertise in Data Science, Machine Learning, Artificial Intelligence, Applied Statistics, Scientific Computing, or Advanced Analytics.
Strong foundation in engineering fundamentals, experimental methods, and quantitative analysis.
Experience designing, executing, and interpreting laboratory, research, or engineering experiments.
Proven ability to apply statistical methods, machine learning, or AI techniques to scientific or engineering problems.
Programming experience with Python and commonly used scientific computing, machine learning, and data analytics libraries.
Strong communication skills with the ability to explain complex technical concepts to audiences with varied levels of expertise.
Passion for solving real-world engineering challenges through a combination of experimentation, data analysis, and scientific reasoning.
Candidates with advanced degrees in Data Science, Statistics, Computer Science, Applied Mathematics, or related quantitative disciplines may also be considered if they have demonstrated experience applying data science techniques to experimental, scientific, manufacturing, semiconductor, or engineering problems.

Nice To Haves

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 Design of Experiments (DOE), Statistical Process Control (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 demonstrated ability to manage multiple technical projects simultaneously.
Excellent collaboration skills with the ability to work effectively in cross-functional and matrixed environments.

Responsibilities

Perform process engineering research, development, characterization, and evaluation in support of Lam's semiconductor capital equipment and systems.
Support new technology development, product qualification, and customer deployment activities.
Design, execute, and analyze experiments to investigate process, hardware, and system-level performance.
Develop advanced test vehicles and apply systematic problem-solving methodologies to improve product capability and reliability.
Compile and evaluate experimental data to establish process understanding and engineering recommendations.
Lead technical investigations associated with customer escalations by combining engineering fundamentals, experimental observations, and advanced analytics.
Apply statistical analysis, engineering judgment, and structured root-cause methodologies to identify failure mechanisms and drive corrective actions.
Partner with cross-functional teams to rapidly resolve product and process issues impacting customer performance.
Develop predictive models and data-driven methodologies that improve understanding of process behavior and equipment performance.
Apply machine learning, artificial intelligence, statistical modeling, and advanced analytics techniques to laboratory, manufacturing, and field data sets.
Develop analytics tools, automated workflows, and visualization methods that enable engineers to efficiently analyze complex datasets.
Utilize data science methodologies to optimize experiments, improve process windows, accelerate root cause analysis, and support technology development.
Collaborate closely with process engineers, hardware engineers, systems engineers, field organizations, and customers to solve challenging technical problems.
Communicate technical findings and recommendations clearly to engineering teams, management, and customer stakeholders.
Contribute to building a culture of scientific rigor and data-driven decision making across the organization.