Battery Algorithm Engineer

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: www.ses.aiWhat 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 is seeking an exceptional Battery Algorithm Engineer to combine materials physics, algorithm development, and big-data systems to create highly accurate battery digital twins, predictive models, and sophisticated optimization engines. This role is central to predicting and enhancing battery safety and performance metrics in real-time. As an Algorithm Engineer, you will be responsible for the full lifecycle of predictive models that power our digital twin battery system.Essential Duties and Responsibilities: Modeling & Algorithm Development Design and develop core physics-based battery models and multi-physics simulations that accurately represent cell behavior. Engineer and apply ML/Deep Learning algorithms (using libraries like TensorFlow) for predictive modeling, safety assessment, and performance optimization. Develop AI4Science algorithms that merge materials physics and computational science to solve complex battery challenges. Digital Twin Architecture Architect and build the digital twin battery system-a virtual battery trained on real-time cell data to continuously monitor and predict safety and performance metrics. Integrate algorithms into big-data systems and infrastructure, ensuring the predictive models are scalable and robust. Domain Bridging & Optimization Maintain a hybrid understanding of data science, materials physics, algorithm infrastructure, and AI models to ensure model validity and utility. Utilize computational tools like COMSOL Multiphysics and finite element analysis (FEA) for complex modeling and simulation tasks. Education and/or Experience: Education: Ph.D. in Materials Engineering or a closely related computational/engineering field. Core Modeling Expertise: Deep foundational knowledge and practical experience with physics-based battery modeling and computational battery modeling. Algorithm Development: Expertise in applying ML/Deep Learning algorithms for predictive modeling and optimization, specifically using libraries such as TensorFlow and other neural network architectures. Technical Stack: Proficiency in core programming languages (Python, MATLAB) and simulation tools (e.g., COMSOL Multiphysics, finite element analysis). Systems Understanding: Experience with algorithm infrastructure and architecting digital twin systems. Preferred Qualifications: Industry Background: Previous experience at battery analytics platforms, electrification R&D centers, or specialized materials/physics ML groups. Big Data Experience: Experience integrating algorithms with large-scale data systems and platforms. Code Management: Familiarity with professional software development practices, including version control using GitHub.