Battery Modeling Engineer - AI for Materials

Apple • Cupertino, CA

1d

About The Position

This position will drive the discovery, design, and optimization of advanced battery materials using Artificial Intelligence/Machine Learning (AI/ML), including Generative AI (GenAI) techniques, and first-principles computational methods (MD/DFT). You will leverage a deep understanding of battery chemistry and physics to accelerate materials innovation for next-generation power solutions. You will work closely with internal multi-functional teams, including materials scientists, experimentalists, and cell engineers, to integrate computational insights with experimental validation to develop cutting-edge battery technologies.

Requirements

• BS degree in Material Science, Chemical, Chemistry, Physics, Computer Science and or related
• Experience with applying AI/ML techniques; machine learning, deep learning, statistical modeling, Generative AI methods or related
• Experience programming skills in languages such as Python or related

Nice To Haves

• Four or more years of experience applying AI/ML, Generative AI, computational chemistry, or computational materials science in the field of battery materials or related energy storage systems.
• Deep understanding of battery electrochemistry, fundamental materials science, and degradation mechanisms for lithium-ion or other advanced battery chemistries.
• Proficiency with AI/ML frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn), with specific experience or strong understanding of Generative AI models (e.g., GANs, VAEs, Diffusion Models, Large Language Models for materials), and data analysis tools (e.g., Pandas, NumPy).
• Hands-on experience with atomistic simulation software (e.g., VASP, LAMMPS, Quantum Espresso, Materials Studio) for MD/DFT calculations.
• Familiarity with materials informatics databases and tools (e.g., Materials Project, OQMD).
• Experience managing and analyzing large datasets from simulations and experiments.
• Ability to translate complex computational results into actionable insights for experimentalists and engineers.
• Strong understanding of multi-physics modeling concepts and their application in electrochemical systems for battery design, performance prediction, and degradation analysis.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level