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Day Computational Material 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 ...

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How much do day computational material science jobs pay per year?

As of Jun 9, 2026, the average yearly pay for day computational material science in the United States is $83,109.00, according to ZipRecruiter salary data. Most workers in this role earn between $77,500.00 and $93,500.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Computational Materials Scientist, and why are they important?

To thrive as a Computational Materials Scientist, you need a strong background in materials science, physics, or chemistry, typically supported by a relevant advanced degree (MSc or PhD). Expertise with simulation software such as VASP, Quantum ESPRESSO, or LAMMPS, as well as proficiency in programming languages like Python or Fortran, is essential. Analytical thinking, problem-solving, and effective communication are standout soft skills for collaborating with interdisciplinary teams and presenting complex data. These skills are crucial for driving innovation, accurately modeling materials, and translating computational results into real-world applications.

What are some common challenges faced by professionals in Day Computational Material Science roles, and how can they be addressed?

Professionals in Day Computational Material Science often encounter challenges such as managing large datasets, integrating experimental and simulation results, and keeping up with rapid advancements in computational methods. Addressing these challenges requires strong collaboration with experimentalists, continual learning to stay updated on new software and algorithms, and effective time management to balance multiple projects. Leveraging open-source tools and participating in interdisciplinary teams can also help overcome technical hurdles and enhance research outcomes.

What is a Computational Material Scientist?

A Computational Material Scientist is a professional who uses computer simulations and theoretical models to study and predict the properties and behaviors of materials. They utilize advanced software and high-performance computing to analyze materials at the atomic or molecular level, helping to design new materials with specific characteristics. Their work supports advancements in fields like electronics, energy, aerospace, and manufacturing by enabling faster and more cost-effective material development compared to traditional experimental methods.

What is the difference between Day Computational Material Science vs Day Materials Engineer?

AspectDay Computational Material ScienceDay Materials Engineer
Required CredentialsTypically requires a PhD or Master's in materials science, physics, or related fieldsBachelor's or Master's in materials engineering or related disciplines
Work EnvironmentPrimarily research labs, computational environments, and simulation softwareDesign, testing, and manufacturing settings, often in industrial or construction sites
Employer & Industry UsageResearch institutions, universities, R&D departments of tech and manufacturing firmsManufacturing companies, construction firms, product development teams

Day Computational Material Science focuses on computer-based simulations and modeling to understand material properties, while Day Materials Engineer involves practical application, testing, and development of materials in real-world settings. Both roles require strong technical knowledge but differ mainly in their work environment and daily tasks.

What cities are hiring for Day Computational Material Science jobs? Cities with the most Day Computational Material Science job openings:
What are the most commonly searched types of Computational Material Science jobs? The most popular types of Computational Material Science jobs are:
What states have the most Day Computational Material Science jobs? States with the most job openings for Day Computational Material Science jobs include:
Day Computational Material Science jobs near you
Computational Materials Scientist

Computational Materials Scientist

SES

Woburn, MA • On-site, Remote

Full-time

Medical

Posted 7 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.

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