Data & Evaluation Applied AI Scientist

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
.

• 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 used to discover and deploy AI‑enhanced battery solutions.

The SES AI Prometheus team is seeking an exceptional Data & Evaluation Applied AI Scientist to serve as the domain expert ensuring that SES AI’s complex battery‑domain knowledge is correctly represented and validated within advanced AI systems, including LLM pipelines and multi‑agent workflows. This role is vital for bridging the gap between raw battery materials knowledge and structured, AI‑trainable data. As the Data & Model Quality Manager, you will focus on the integrity, structure, and fidelity of the knowledge embedded within our AI systems.

• Data Curation & Validation
  • Translate deep battery materials knowledge and next‑generation battery concepts into correctly structured, high‑quality, AI‑trainable data.
  • Lead processes for rigorous data validation, cleaning, and annotation to ensure consistency and correctness across all datasets.
  • Oversee the creation and management of benchmark datasets and design domain‑specific multimodal evaluations to test model accuracy.
• AI System Quality & Correctness
  • Partner closely with AI architecture and engineering teams to ensure the correctness, reliability, and scientific reasoning quality of models, including LLM creation and multi‑agent orchestration.
  • Implement techniques, including those inspired by reinforcement learning (RLHF), to tune and validate model behavior against established scientific principles.
  • Ensure that resulting models accurately understand molecular chemistry, materials data, and complex scientific reasoning in the battery domain.
  • Drive the application of Battery Informatics principles across all data pipelines and modeling efforts.

• Education: Ph.D. in Chemical Engineering with a focus on Lithium battery systems, Materials Science, or a closely related computational/domain field.
• Domain Expertise: Deep expertise in battery materials, particularly knowledge required to convert complex, real‑world data into AI‑trainable formats.
• Data Quality & Validation: Proven experience in data validation, annotation, and benchmark creation for complex scientific or engineering datasets.
• AI Exposure: Experience working with advanced AI systems, including familiarity with LLM pipelines and the principles of multi‑agent orchestration.
• Applicable Background: Experience in roles such as Applied Scientist in Molecular/Materials AI or similar specialist roles focused on AI system quality in a scientific domain.

• Advanced AI Techniques: Experience with specialized techniques used for model tuning and alignment, such as Reinforcement Learning from Human Feedback (RLHF).
• Industry Precedent: Previous experience in specialized environments like battery focus labs, materials data science groups, or AI4
  
  Science teams with a focus on agent pipeline building and model tuning (e.g., drawing from precedents like Deep Mind or Fair Labs).
• Evaluation Design: Direct experience designing and executing domain‑specific multimodal evaluations for complex AI models.
• Computational Focus: Experience as a Computational battery AI specialist.