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:
What we Need:
The SES AI Hermes team is seeking an exceptional Battery Electrolyte Scientist & Materials Chemistry Leader to serve as an R&D leader specializing in electrolyte development and interfacial chemistry. This role is critical for producing experimentally validated battery component data, guiding AI model training, and defining the materials innovation roadmap for next-generation energy storage systems. As an Electrolyte Scientist and R&D Leader, you will direct key experimental programs and manage the strategic interface between chemistry and AI.
Essential Duties and Responsibilities:
R&D Program Leadership
Direct complex electrolyte R&D programs focused on next-generation battery systems, ensuring alignment with customer and product requirements (e.g., safety, cyclability, low/high temperature performance).
Lead multi-R&D teams composed of chemists, data scientists, and engineers to achieve key innovation milestones.
Define the materials innovation roadmap, particularly related to electrolyte formulation and interfacial science.
Experimental Validation & Data Generation
Oversee SEI (Solid Electrolyte Interphase) engineering and additive optimization programs, leveraging expertise in fundamental materials chemistry.
Lead cell-level validation and the integration of new electrolytes into prototype cells.
Produce high-quality, experimentally validated battery component data, guiding AI model training by ensuring data quality, experimental reproducibility, and comprehensive parameter space coverage (Data-driven experimentation).
AI Integration & Tools
Utilize molecular informatic tools to accelerate battery component optimization cycles, effectively bridging experimental data with AI-generated candidates.
Education and/or Experience:
Preferred Qualifications: