Summer 2027 - Process Simulation Engineering Intern - MS/PhD

The group you’ll be a part of

The Office of the CTO is where innovation takes center stage. We inspire our global technical community to take on grand challenges, understand emerging trends, identify the critical inflections, and drive our sustainability, Environment, Social, and Governance (ESG) practices that will define the next generation of semiconductors and continued impact.

The impact you’ll make

As a Process Simulation Engineer Intern, you’ll help build a virtual twin of plasma etch processes. You’ll develop physics-based models of how plasma etching works and fast machine-learning surrogate versions of those models, together connecting tool settings to predicted feature profiles. Your work will directly support data-driven process development for Lam’s etch products.

What you’ll do

• Build and refine models of how plasma etches a surface: ion and neutral species interactions with the wafer, sticking and reaction probabilities, redeposition, and ion energy and angle effects.
• Calibrate model parameters against measured profiles (CD vs. depth from SEM/TEM images).
• Design simulation experiments (parameter sweeps, design of experiments) to generate training data.
• Build and validate fast surrogate models of the slower physics simulations, including checks on accuracy and uncertainty.
• Use surrogates to explore which settings matter most and to optimize process conditions.
• Write Python tools to automate simulation runs, data processing, and plotting.
• Document the workflow and present results at the end of the internship.

Who we’re looking for

• Currently pursuing an MS or PhD in chemical engineering, physics, materials science, chemistry, or a related field.
• Strong Python skills, including hands-on experience building and training models in PyTorch (or a similar framework such as JAX or TensorFlow).
• Background in reaction kinetics, transport phenomena, or surface/plasma science.
• Hands-on experience building machine-learning or regression models.
• Strong analytical and communication skills, and the ability to work effectively in a team.
• Note this is an onsite-flex position (3+ day in the office).

Preferred qualifications

• Coursework or research in plasma physics or plasma-surface interactions.
• Experience with surrogate modeling methods: Gaussian processes, neural networks, polynomial chaos, or reduced-order models.
• Experience with GPyTorch/BoTorch, physics-informed neural networks, or uncertainty quantification.
• Experience with design of experiments, sensitivity analysis, Bayesian calibration, or optimization.
• Experience with physics-based simulation tools (finite element, Monte Carlo, or feature-scale/profile simulators).
• Familiarity with semiconductor etch processes or SEM/TEM image analysis.

Our commitment

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.