Data Scientist 4

The Office of the CTO is where innovation takes center stage at Lam Research. We inspire our global technical community to take on grand challenges, explore emerging trends, identify critical technology inflections, and develop cutting-edge computational solutions that define the next generation of semiconductors.

Within Semiverse Solutions at Lam Research, we build software products and virtual twins that revolutionize semiconductor fabrication. Our solutions combine physical simulations-such as multi-physics plasma modeling and 3D feature scale modeling-with modern machine learning to solve critical engineering challenges.

We are looking for a senior Data Scientist to join our team at the intersection of computational physics and machine learning. In this role, you will own the development of surrogate models, virtual twins, and optimization workflows end-to-end: from designing numerical experiments and generating synthetic data to deploying clean, maintainable Python code into our commercial software.

• Build and scale physics-aware machine learning models and surrogate models to accelerate complex multi-physics and process simulations.
• Apply core data science, statistical analysis, and machine learning methods across both simulated and experimental datasets.
• Implement optimization algorithms, such as Bayesian optimization and active learning, to accelerate process design and calibration.
• Run simulations to generate training data and design effective sampling strategies for model development.
• Write robust, production-quality Python code following modern software engineering practices.
• Collaborate closely with computational physicists, semiconductor process engineers, and software developers to turn complex physics problems into practical ML solutions.

• Education & Experience: Master's degree with 6+ years of relevant experience, or Ph.D. with 3+ years in Physics, Engineering, Applied Mathematics, Computer Science, or a related quantitative field.
• Hands-on Data Science & ML: Demonstrated ability to build, train, evaluate, and tune machine learning models end-to-end on real-world datasets, backed by solid foundations in statistics and core ML algorithms.
• Scientific Machine Learning: Experience building surrogate models, physics-aware ML architectures, or optimization algorithms (e.g., Bayesian optimization, Gaussian processes).
• Physics & Simulation Background: Strong understanding of physical sciences (e.g., transport phenomena, kinetics, or semiconductor process/device physics) and comfort working with numerical simulation data.
• Software Engineering & Problem Solving: Strong Python programming skills, comfortable solving algorithmic problems and writing modular, testable, and maintainable code.

• Professional or academic experience within the semiconductor industry, semiconductor processing, or device physics.
• Familiarity with multi-physics solvers, numerical simulation tools, or scientific computing pipelines.
• Experience with HPC environments, workload schedulers (e.g., SLURM), or distributed compute.

We believe it is important for every person to feel valued, included, and empowered to achieve their full potential. By bringing unique individuals and viewpoints together, we achieve extraordinary results.

Lam Research ("Lam" or the "Company") is an equal opportunity employer. Lam is committed to and reaffirms support of equal opportunity in employment and non-discrimination in employment policies, practices and procedures on the basis of race, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex (including pregnancy, childbirth and related medical conditions), gender, gender identity, gender expression, age, sexual orientation, or military and veteran status or any other category protected by applicable federal, state, or local laws. It is the Company's intention to comply with all applicable laws and regulations. Company policy prohibits unlawful discrimination against applicants or employees.

Lam offers a variety of work location models based on the needs of each role. Our hybrid roles combine the benefits of on-site collaboration with colleagues and the flexibility to work remotely and fall into two categories - On-site Flex and Virtual Flex. 'On-site Flex' you'll work 3+ days per week on-site at a Lam or customer/supplier location, with the opportunity to work remotely for the balance of the week. 'Virtual Flex' you'll work 1-2 days per week on-site at a Lam or customer/supplier location, and remotely the rest of the time.