Computational Materials Scientist

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: [](http://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