Scientist, Molecular AI Architecture

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.ai 

 

What 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 Scientist, Molecular AI Architecture to pioneer the convergence of biological computation, large-scale multimodal foundation models, and explainable AI architectures. This visionary role will drive next-generation materials discovery by developing novel AI systems. 

 

 

Essential Duties and Responsibilities: 

• AI Architecture & Design: 

• Architect novel AI systems and deep neural architectures (e.g., Transformers, CNNs) inspired by principles of systems neuroscience and neural coding principles. 

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