Senior Staff Computational Materials Engineer - MLIPs

LAM RESEARCH Corporation

Fremont (CA)

Hybrid

USD 166,000 - 350,000

Full time

14 days+

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Job summary

Lam Research in California seeks a Senior Staff Computational Materials - MLIPs Engineer to advance atomistic simulations and ML-driven process insights. You will lead chemistry modeling, MLIP development, and scalable data workflows for semiconductor applications.

Ideal candidates have a PhD, 8+ years of experience, and strong Python/HPC skills, with expertise in DFT tools and reactive MD. Hybrid on-site/remote work options are available.

Qualifications

  • PhD in Computational Chemistry, Chemistry, Chemical Engineering or Materials Science.
  • 8+ years of industry experience after PhD.
  • Experience using DFT software (Gaussian, Quantum Espresso).
  • Experience with MLIP architectures and reactive MD (ReaxFF).
  • Proficiency in Python and HPC for large-scale simulations.

Responsibilities

  • Perform first-principles calculations and atomistic modeling of reaction mechanisms and surface chemistry.
  • Generate high-fidelity training data from quantum chemistry/DFT for ML-driven simulations.
  • Design, train, validate and deploy MLIP architectures for predictive simulations.
  • Build scalable workflows for data generation, active learning, and uncertainty quantification.
  • Communicate complex results to stakeholders and guide process specifications.

Skills

communication
team collaboration

Education

Ph.D. in Computational Chemistry or related field

Tools

Gaussian
Quantum Espresso
Python
ReaxFF
MLIP architectures (MACE, SevenNet)
HPC environments

Job description

The group you’ll be a part of

In the Semiverse Solutions Team, we are dedicated to excellence in the virtual experimentation of Lam’s etch and deposition processes. We drive innovation to ensure our cutting-edge solutions are helping to solve the biggest challenges in the semiconductor industry.

The impact you’ll make

As a Senior Staff Computational Materials - MLIPs Engineer at Lam, you will operate on cutting-edge technology, harnessing atomic precision, materials science, and surface engineering to push technical boundaries. Your role involves identifying new and advanced processes and chemical formulations. Your expertise and knowledge will play a crucial role in our customers’ success, making an impact on next generation semiconductor technologies.

What you’ll do
Computational Chemistry
  • Perform first-principles calculations and atomistic modeling to investigate reaction mechanisms, surface chemistry, plasma-surface interactions, and materials behavior
  • Generate high fidelity training datasets from quantum chemistry and density functional theory (DFT) calculations to support development of next-generation simulation capabilities
  • Utilize computational chemistry learning to support process engineering research & development, and process/chamber/feature simulations
  • Compile and evaluate modeling data to provide guidance on chemistries and materials, as well as appropriate limits and variables for process specifications
  • Communicate chemistry and process insights clearly through presentations and technical discussions with stakeholders
Machine Learning for Atomistic Simulations
  • Evaluate machine learning interatomic potential (MLIP) architectures (e.g. MACE, SevenNet)
  • Design, train, validate and deploy MLIP architectures using DFT reference data to establish predictive molecular dynamics simulations for various semiconductor device materials and process chemistry applications
  • Build scalable workflows for data generation, active learning, model training, uncertainty quantification, and validation of ML-based force fields
Software/Infrastructure
  • Collaborate with software engineers and domain scientists to integrate MLIP capabilities into simulation platforms and digital twin solutions
Leadership
  • Provide technical leadership in computational materials science, computational chemistry, and machine learning methodologies; mentor engineers and influence cross-functional technology roadmaps
  • Drive identification, evaluation, and adoption of emerging simulation and AI technologies that create strategic advantage in semiconductor process development
Who we’re looking for
  • Ph.D. in Computational Chemistry, Chemistry, Chemical Engineering or Materials Science (or equivalent)
  • 8+ years of industry experience, post Ph.D.
  • Experience using DFT software (e.g. Gaussian, Quantum Espresso)
  • Experience with frontier molecular orbital analysis and full reaction pathway studies
  • Experience applying quantum chemistry fundamentals to solve challenges related to semiconductor processes and materials applications
  • Experience developing, training, validating, and deploying MLIP architectures (e.g. MACE, SevenNet)
  • Experience with reactive molecular dynamics (e.g. ReaxFF)
  • Proficiency in scientific programming using Python, or related languages
  • Experience utilizing high performance computing (HPC) environments for large-scale simulations and data analysis
  • Demonstrated ability to independently solve complex technical problems and communicate results to multidisciplinary teams
  • Strong organizational skills and demonstrated ability to manage multiple tasks simultaneously
  • Ability to react to shifting priorities to meet business needs and deadlines
Preferred qualifications
  • Experience defining technical strategy for atomistic simulations, machine learning, or scientific computing capabilities
  • Experience translating advanced simulation and AI technologies into engineering solutions that impact product development
  • Experience leading collaborations with universities, national laboratories, or external technology partners
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.

Salary

CA San Francisco Bay Area Salary Range for this position: $166,000.00 - $350,000.00.

The above salary range for this position is relevant to applicants that reside or work onsite in the California, San Francisco Bay Area only. Salary offers will depend on factors that include the location you work from, your level, education, training, specific skills, years of experience and comparison to other employees already in this role. Actual salary may vary from salary offered due to numerous factors including but not limited to unpaid time off, unpaid leave, company mandated shutdown, and other relevant factors.

Our Perks and Benefits

At Lam, our people make amazing things possible. That’s why we invest in you throughout the phases of your life with a comprehensive set of outstanding benefits.

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