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Internship Computational Materials Science Jobs (NOW HIRING)

SES

Computational Materials Scientist

Woburn, MA · On-site +1

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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How much do internship computational materials science jobs pay per hour?

As of Jun 9, 2026, the average hourly pay for internship computational materials science in the United States is $19.31, according to ZipRecruiter salary data. Most workers in this role earn between $16.11 and $20.91 per hour, depending on experience, location, and employer.

What is an internship in computational materials science?

An internship in computational materials science is a temporary position, usually for students or recent graduates, where you gain hands-on experience using computer simulations and modeling to study and design materials. Interns typically work on research projects involving software tools to predict material properties, analyze data, or develop new materials. This experience provides valuable skills in programming, data analysis, and scientific research, which are essential for a career in materials science or related fields.

What is the difference between Internship Computational Materials Science vs Computational Materials Scientist?

AspectInternship Computational Materials ScienceComputational Materials Scientist
CredentialsEnrolled in or recent graduate of relevant degree programsAdvanced degree (Master's or Ph.D.) in materials science, physics, or related fields
Work EnvironmentAcademic or research labs, internships at industry companiesResearch and development teams in industry or academia
ResponsibilitiesAssisting with computational modeling, data analysis, learning industry toolsLeading projects, developing models, publishing research

Internship Computational Materials Science positions are entry-level, focused on learning and supporting ongoing projects, while Computational Materials Scientists are experienced professionals responsible for independent research and development in the field.

What types of projects can an intern expect to work on in a Computational Materials Science internship?

As a Computational Materials Science intern, you can expect to be involved in projects such as simulating material properties, analyzing large datasets from experiments, or developing and testing computational models. Interns often assist with software coding, data visualization, and running simulations using tools like Density Functional Theory (DFT) or molecular dynamics packages. Collaboration with other scientists and engineers is common, and you may contribute to research publications or presentations, providing valuable hands-on experience in both individual and team-based research settings.

What are the key skills and qualifications needed to thrive as an Internship Computational Materials Science, and why are they important?

To thrive as an intern in Computational Materials Science, you typically need a solid background in materials science, physics, or related fields, along with programming skills in languages such as Python or C++. Familiarity with computational tools like Density Functional Theory (DFT) software (e.g., VASP, Quantum ESPRESSO) and data analysis platforms is highly beneficial. Strong analytical thinking, attention to detail, and effective communication set candidates apart in collaborative research environments. These competencies enable interns to contribute meaningfully to simulations, data interpretation, and innovative materials research.
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Internship Computational Materials Science jobs near you
Infographic showing various Internship Computational Materials Science job openings in the United States as of May 2026, with employment types broken down into 24% Internship, 44% Full Time, 19% Part Time, 6% Temporary, 3% Contract, and 4% Nights. Highlights an 74% Physical, 3% Hybrid, and 23% Remote job distribution, with an average salary of $40,174 per year, or $19.3 per hour.
Computational Materials Scientist

Computational Materials Scientist

SES

Woburn, MA • On-site, Remote

Full-time

Medical

Posted 6 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.

Apply