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Intern Computational Materials Science Jobs (NOW HIRING)

SES

Computational Materials Scientist

Woburn, MA · On-site +1

$180K - $200K/yr

This powerful combination of "AI for science" and material engineering enables batteries that can ... As a Computational Materials Scientist, you will be a core data-driven modeler responsible for ...

Mat3ra

Computational Materials Scientist

Walnut Creek, CA · On-site +1

$90K - $140K/yr

Overview We look for computational materials scientists excited about bridging the gap between ... science to help us develop a software framework for designing and discovering new advanced ...

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Intern Computational Materials Science information

What is the difference between Intern Computational Materials Science vs Intern Materials Engineering?

AspectIntern Computational Materials ScienceIntern Materials Engineering
Required CredentialsUndergraduate or graduate in materials science, physics, or related fields; basic programming skillsUndergraduate or graduate in materials engineering, mechanical engineering, or related fields; foundational technical knowledge
Work EnvironmentResearch labs, computational modeling, data analysisDesign, testing, and development in labs or manufacturing settings
Industry UsageResearch institutions, tech companies, aerospace, academiaManufacturing firms, product development, construction

Intern Computational Materials Science focuses on computational modeling and simulations of materials properties, while Intern Materials Engineering emphasizes practical design, testing, and application of materials. Both roles require a background in materials-related fields but differ in their core activities and work environments.

What does an intern in computational materials science do?

An Intern in Computational Materials Science assists in research and development by applying computational techniques to study and predict the properties and behaviors of materials. Typical tasks include running simulations, analyzing data, and working with software tools to model materials at the atomic or molecular level. Interns may collaborate with researchers to design experiments, interpret results, and contribute to scientific publications or reports. This role provides hands-on experience in both computational methods and materials science, helping to bridge theory and practical application.

What are the key skills and qualifications needed to thrive as an intern in computational materials science, and why are they important?

To thrive as an Intern in Computational Materials Science, you need a solid background in materials science, physics, or engineering, along with coursework in computational modeling and data analysis. Familiarity with programming languages like Python or MATLAB, experience with simulation software (such as VASP or LAMMPS), and knowledge of high-performance computing are typically required. Strong analytical thinking, attention to detail, and effective teamwork are important soft skills for success in collaborative research environments. These skills enable interns to contribute meaningfully to research projects, analyze complex materials data, and communicate findings clearly within multidisciplinary teams.

What types of projects can an intern in computational materials science expect to work on during their internship?

As an Intern in Computational Materials Science, you can expect to engage in projects involving simulations of material properties, data analysis from computational experiments, and the development of models to predict material behavior. You may collaborate with researchers and senior scientists to support ongoing investigations or help optimize simulation workflows. These projects often require proficiency in programming languages such as Python or MATLAB and may involve the use of specialized software like VASP or LAMMPS. The experience provides a hands-on understanding of how computational methods contribute to advancing materials research and often includes opportunities to present your findings to the team.
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Infographic showing various Intern Computational Materials Science job openings in the United States as of August 2026, with employment types broken down into 23% Internship, 38% Full Time, 31% Part Time, and 8% Temporary. Highlights an 69% In-person, 8% Hybrid, and 23% Remote job distribution.

Computational Materials Scientist

SES

Woburn, MA • On-site, Remote

$180K - $200K/yr

Full-time

Medical

Re-posted 12 days ago

Job description

SES AI Corp. (NYSE: SES) is dedicated to accelerating the world's energy transition through groundbreaking material discovery and advanced battery management. We are at the forefront of revolutionizing battery creation, pioneering the integration of cutting-edge machine learning into our research and development. Our AI-enhanced, high-energy-density and high-power-density Li-Metal and Li-ion batteries are unique; they are the first in the world to utilize electrolyte materials discovered by AI. This powerful combination of "AI for science" and material engineering enables batteries that can be used across various applications, including transportation (land and air), energy storage, robotics, and drones.
To learn more about us, please visit: www.ses.ai
What We Offer:
  • A highly competitive salary and robust benefits package, including comprehensive health coverage and an attractive equity/stock options program within our NYSE-listed company.
  • The opportunity to contribute directly to a meaningful scientific project-accelerating the global energy transition-with a clear and broad public impact.
  • Work in a dynamic, collaborative, and innovative environment at the intersection of AI and material science, driving the next generation of battery technology.
  • Significant opportunities for professional growth and career development as you work alongside leading experts in AI, R&D, and engineering.
  • Access to state-of-the-art facilities and proprietary technologies are used to discover and deploy AI-enhanced battery solutions.

What we Need:
The SES AI Prometheus team isseeking an exceptional Computational Materials Scientist to combine physics-based simulation (DFT, MD, quantum modeling) with AI-assisted material prediction to generate high-quality training data and accelerate materials discovery. This role is crucial for advancing our understanding of electrochemical energy materials at the atomic level. As a Computational Materials Scientist, you will be a core data-driven modeler responsible for executing and automating complex simulations.
Essential Duties and Responsibilities:
  • Atomistic Modeling & Simulation
  • Conduct and oversee DFT (Density Functional Theory), MD (Molecular Dynamics), and QM (Quantum Mechanics) simulations of battery components, including electrolytes, coatings, and electrodes.
  • Develop and refine ML-enhanced force fields and surrogate models to accelerate simulation time scales and enable multi-scale simulation efforts.
  • Apply expertise in atomistic simulation and quantum modeling to solve key challenges in electrochemical energy materials (e.g., batteries/fuel cells).
  • AI Data Generation & Prediction
  • Generate high-quality, structured simulation data to serve as training sets for AI property prediction models and material screening modules.
  • Contribute to the development of battery domain LLM features and advanced property-prediction models.
  • Automate complex simulation workflows using strong coding practices to enhance efficiency and scalability.
  • Collaboration & Tooling
  • Collaborate with experimental teams, leveraging a hybrid computational + experimental literacy to validate models and drive design iteration.
  • Utilize advanced simulation tools (VASP, Quantum Espresso) and data science libraries (TensorFlow, Pandas) to manage and analyze large datasets.

Education and/or Experience:
  • Education: Ph.D. in Mechanical Engineering, Materials Science, Chemical Engineering, or a closely related computational/physics field.
  • Core Simulation Expertise: Deep and extensive experience in atomistic simulation and quantum modeling, including proficiency with key QM/DFT tools (VASP, Quantum Espresso) and MD simulations.
  • Domain Focus: Strong background in electrochemical energy materials and extensive computational work focused on batteries/fuel cells.
  • Coding Proficiency: Strong coding skills in Python (along with related libraries like Pandas and TensorFlow) for simulation workflow automation and data analysis.
  • ML Application: Experience in developing or utilizing ML-enhanced force fields and surrogate models for materials prediction., or equivalent practical experience.

Preferred Qualifications:
  • LLM Development: Experience in developing battery domain LLM features or property-prediction models.
  • Hybrid Skillset: Demonstrated experience working in a hybrid computational + experimental environment.
  • Tooling Diversity: Familiarity with additional data analysis tools like R, SQL, MATLAB, and time-series forecasting libraries like Prophet.
  • Target Background: Previous experience at national laboratories, XtalPi, Entalpic, or deep battery modeling groups.

The salary range for this position as required under applicable pay transparency laws.
Salary Range
$180,000-$200,000 USD

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