Battery Modeling Engineer - AI for Materials

Role Number: 200648552-0836SummaryDo you love working on challenges that no one has solved yet? Do you like changing the game? Envision what you could do here. At Apple, we believe new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish.DescriptionThis 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.Minimum Qualifications 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 Preferred Qualifications 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. Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant (.

Created: 2026-03-17