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.

• Design and implement large-scale multimodal foundation models and agentic AI systems capable of complex reasoning over molecular and battery datasets.

• Develop methods for model interpretability, representation engineering, and causal reasoning to ensure AI results are explainable and trustworthy for materials science.

• High-Performance Computing & Efficiency

• Lead software development efforts for high-performance computing (HPC), focusing heavily on GPU programming and scaling the training and inference efficiency of large neural networks.

• Optimize complex ML frameworks (like JAX) within systems and cluster computing environments (e.g., Singularity).

• Scientific ML Integration

• Create automated data-labeling and behavioral encoding models specifically designed to enhance Molecular AI training and data efficiency.

• Apply Scientific ML principles to complex molecular and battery datasets, translating biological computation concepts into practical AI solutions for materials discovery.

Education and/or Experience:

• Education: Ph.D. in Computational and Systems Biology, Computational Neuroscience, or a closely related quantitative field.

• Core Expertise: Deep, demonstrated expertise in systems neuroscience, machine learning, and the design and implementation of deep neural architecture.

• HPC Software: Proven experience with software development for High-Performance Computing (HPC) environments, including expert-level GPU programming.

• Model Design: Practical experience in designing and training foundation models and working with concepts like multi-agent reasoning models.

• Interpretability Focus: Demonstrated work in model interpretability and representation engineering applied to complex scientific data.

Preferred Qualifications:

• Specialized Frameworks: Practical experience with advanced mathematical and machine learning frameworks like JAX and Julia.

• Advanced Techniques: Expertise in Bayesian inference and working within specialized container/computing environments like Singularity.

• Advanced AI: Experience with the design and application of agentic AI systems and multimodal reasoning architectures.