Computational Materials Scientist

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 used to discover and deploy AI-enhanced battery solutions.

The SES AI Prometheus team is seeking 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.

• Atomistic Modeling & Simulation
• Conduct and oversee DFT, MD, and QM 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.
• 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 (Tensor Flow, Pandas) to manage and analyze large datasets.

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

• Experience in developing battery domain LLM features or property-prediction models.
• Demonstrated experience working in a hybrid computational + experimental environment.
• Familiarity with additional data analysis tools like R, SQL, MATLAB, and time-series forecasting libraries like Prophet.
• Previous experience at national laboratories, Xtal Pi, Entalpic, or deep battery modeling groups.

The salary range for this position is required under applicable pay transparency laws.

Salary Range: $180,000 USD - $200,000 USD