SES AI
Overview
Battery Algorithm Engineer at SES AI Corp. (NYSE: SES).
What We Offer
Competitive salary, comprehensive health coverage, attractive equity/stock options program.
Meaningful scientific project with broad public impact.
Dynamic, collaborative, innovative environment at intersection of AI and material science.
Professional growth and career development with leading experts.
State‑of‑the‑art facilities and proprietary technologies for AI‑enhanced battery solutions.
What We Need Prometheus team seeks exceptional Battery Algorithm Engineer to combine materials physics, algorithm development, and big‑data systems to create digital twins, predictive models, and optimization engines.
Essential Duties And Responsibilities
Design and develop core physics‑based battery models and multi‑physics simulations.
Engineer and apply ML/Deep Learning algorithms (TensorFlow, other neural networks) for predictive modeling, safety assessment, and performance optimization.
Merge materials physics and computational science in AI4Science algorithms for complex battery challenges.
Architect and build the digital twin battery system—virtual battery trained on real‑time cell data to monitor and predict safety and performance metrics.
Integrate algorithms into big‑data systems and infrastructure, ensuring scalability and robustness.
Maintain hybrid understanding of data science, materials physics, algorithm infrastructure, and AI models for model validity and utility.
Utilize computational tools like COMSOL Multiphysics and finite element analysis (FEA) for complex modeling and simulation tasks.
Education And Experience
Ph.D. in Materials Engineering or closely related computational/engineering field.
Deep foundational knowledge and practical experience with physics‑based battery modeling and computational battery modeling.
Expertise in applying ML/Deep Learning algorithms for predictive modeling and optimization using TensorFlow and other neural network architectures.
Proficiency in Python, MATLAB, and simulation tools (COMSOL Multiphysics, FEA).
Experience with algorithm infrastructure and architecting digital twin systems.
Preferred Qualifications
Previous experience at battery analytics platforms, electrification R&D centers, or specialized materials/physics ML groups.
Experience integrating algorithms with large‑scale data systems and platforms.
Familiarity with professional software development practices, including GitHub version control.
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What We Offer
Competitive salary, comprehensive health coverage, attractive equity/stock options program.
Meaningful scientific project with broad public impact.
Dynamic, collaborative, innovative environment at intersection of AI and material science.
Professional growth and career development with leading experts.
State‑of‑the‑art facilities and proprietary technologies for AI‑enhanced battery solutions.
What We Need Prometheus team seeks exceptional Battery Algorithm Engineer to combine materials physics, algorithm development, and big‑data systems to create digital twins, predictive models, and optimization engines.
Essential Duties And Responsibilities
Design and develop core physics‑based battery models and multi‑physics simulations.
Engineer and apply ML/Deep Learning algorithms (TensorFlow, other neural networks) for predictive modeling, safety assessment, and performance optimization.
Merge materials physics and computational science in AI4Science algorithms for complex battery challenges.
Architect and build the digital twin battery system—virtual battery trained on real‑time cell data to monitor and predict safety and performance metrics.
Integrate algorithms into big‑data systems and infrastructure, ensuring scalability and robustness.
Maintain hybrid understanding of data science, materials physics, algorithm infrastructure, and AI models for model validity and utility.
Utilize computational tools like COMSOL Multiphysics and finite element analysis (FEA) for complex modeling and simulation tasks.
Education And Experience
Ph.D. in Materials Engineering or closely related computational/engineering field.
Deep foundational knowledge and practical experience with physics‑based battery modeling and computational battery modeling.
Expertise in applying ML/Deep Learning algorithms for predictive modeling and optimization using TensorFlow and other neural network architectures.
Proficiency in Python, MATLAB, and simulation tools (COMSOL Multiphysics, FEA).
Experience with algorithm infrastructure and architecting digital twin systems.
Preferred Qualifications
Previous experience at battery analytics platforms, electrification R&D centers, or specialized materials/physics ML groups.
Experience integrating algorithms with large‑scale data systems and platforms.
Familiarity with professional software development practices, including GitHub version control.
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