Computational Materials Scientist

SES AI Corp

Computational Materials Scientist combining physics-based simulation and AI for energy materials discovery at SES. Focused on advancing battery technology through innovative research and development in a collaborative environment.

Posted 6/24/2026full-timeWoburn • Massachusetts • 🇺🇸 United StatesMid-LevelSenior💰 $180,000 - $200,000 per yearWebsite

Tech Stack

Tools & technologies

PandasPythonTensorflow

About the role

Key responsibilities & impact

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).
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.

Requirements

What you’ll need

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.

Benefits

Comp & perks

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.

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Hard Skills & Tools

DFTMolecular DynamicsQuantum Mechanicsatomistic simulationquantum modelingML-enhanced force fieldssurrogate modelsPythondata analysissimulation workflow automation

Soft Skills

collaborationcommunicationproblem-solvingdesign iterationinterdisciplinary literacy

Certifications

Ph.D. in Mechanical EngineeringPh.D. in Materials SciencePh.D. in Chemical Engineering