Who We Are
Applied Materials is a global leader in materials engineering solutions used to produce virtually every new chip and advanced display in the world. We design, build and service cutting-edge equipment that helps our customers manufacture display and semiconductor chips - the brains of devices we use every day. As the foundation of the global electronics industry, Applied enables the exciting technologies that literally connect our world - like AI and IoT. If you want to push the boundaries of materials science and engineering to create next generation technology, join us to deliver material innovation that changes the world.
What We Offer
Salary:
$138,000.00 - $190,000.00
Location:
Santa Clara,CA
You'll benefit from a supportive work culture that encourages you to learn, develop, and grow your career as you take on challenges and drive innovative solutions for our customers. We empower our team to push the boundaries of what is possible-while learning every day in a supportive leading global company. Visit our Careers website to learn more.
At Applied Materials, we care about the health and wellbeing of our employees. We're committed to providing programs and support that encourage personal and professional growth and care for you at work, at home, or wherever you may go. Learn more about our benefits (https://hrportal.ehr.com/applied/) .
We are seeking a highly skilled Computational Materials Scientist with deep expertise in machine learning for atomistic modeling, specifically in reactive machine-learned interatomic potentials (MLIPs). This role focuses on developing and deploying ML-driven models capable of accurately capturing bond breaking, bond formation, and complex chemical reactions, enabling predictive simulations at near first-principles accuracy with significantly improved scalability.
The ideal candidate will combine physics-based understanding, advanced machine learning techniques, and strong analytical reasoning to solve challenging problems in materials design and process development.
Key Responsibilities
Develop, train, and deploy reactive MLIPs to model chemical reactions, interfacial processes, and dynamic material behavior.
Build ML models capable of predicting energies, forces, and reaction pathways with near DFT-level accuracy.
Generate and curate high-quality training datasets from DFT and other first-principles methods.
Design and implement active learning workflows to iteratively improve model robustness and coverage of configuration space.
Integrate MLIPs with molecular dynamics (MD) to simulate:
Reactive processes
Diffusion and transport
Oxidation/reduction
Surface and interface evolution
Apply enhanced sampling techniques (e.g., NEB, metadynamics) in combination with ML models for reaction pathway exploration.
Develop automated simulation pipelines and scalable workflows for high-throughput studies.
Analyze large datasets to extract structure-property and structure-reactivity relationships.
Collaborate cross-functionally with experimental, process, and device teams to guide materials and process optimization.
Required Qualifications
Ph.D. in Materials Science, Physics, Chemistry, or related field.
Demonstrated expertise in reactive machine-learned interatomic potentials (MLIPs) capable of modeling bond breaking and formation.
Hands-on experience with one or more MLIP frameworks:
MACE, NequIP, GAP, SNAP, DeepMD, or equivalent
Strong background in first-principles methods (DFT) and atomistic simulations (MD).
Proficiency in Python and ML frameworks (PyTorch, TensorFlow).
Experience working in HPC environments and handling large-scale simulations.
Proven ability in dataset generation, labeling strategies, and model validation for ML-based atomistic models.
Core Technical Competencies
Reactive MLIP development and deployment
Machine learning for atomistic simulations
Molecular dynamics and reaction modeling
Materials informatics and data pipelines
High-performance scientific computing
Analytical & Reasoning Requirements
Strong analytical, logical reasoning, and quantitative problem-solving skills.
Demonstrated ability to:
Diagnose and debug ML model failures and training instabilities
Critically evaluate model predictions against physical principles
Ensure physical consistency, transferability, and robustness of simulations
Identify gaps in training data and design targeted data acquisition strategies
Ability to translate complex physical phenomena into tractable computational models.
Preferred Qualifications
Experience in reactive systems, including:
Surface chemistry
Catalysis
Oxidation/reduction reactions
Semiconductor or interface materials
Familiarity with uncertainty quantification, Bayesian methods, and active learning.
Experience with:
ASE, LAMMPS, VASP, or similar tools
Workflow frameworks (FireWorks, AiiDA, etc.)
Exposure to graph neural networks (GNNs) and equivariant architectures.
Industry experience in materials development or process modeling.
Additional Information
Time Type:
Full time
Employee Type:
New College Grad
Travel:
Yes, 10% of the Time
Relocation Eligible:
Yes
The salary offered to a selected candidate will be based on multiple factors including location, hire grade, job-related knowledge, skills, experience, and with consideration of internal equity of our current team members. In addition to a comprehensive benefits package, candidates may be eligible for other forms of compensation such as participation in a bonus and a stock award program, as applicable.
For all sales roles, the posted salary range is the Target Total Cash (TTC) range for the role, which is the sum of base salary and target bonus amount at 100% goal achievement.
Applied Materials is an Equal Opportunity Employer. Qualified applicants will receive consideration for employment without regard to race, color, national origin, citizenship, ancestry, religion, creed, sex, sexual orientation, gender identity, age, disability, veteran or military status, or any other basis prohibited by law.
In addition, Applied endeavors to make our careers site (https://www.appliedmaterials.com/us/en/careers.html) accessible to all users. If you would like to contact us regarding accessibility of our website or need assistance completing the application process, please contact us via e-mail at Accommodations_Program@amat.com, or by calling our HR Direct Help Line at 877-612-7547, option 1, and following the prompts to speak to an HR Advisor. This contact is for accommodation requests only and cannot be used to inquire about the status of applications.
