Machine Learning Scientist, AI Explainability
About Us:
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 (AI Research) is seeking an exceptional Machine Learning Scientist to spearhead the development of our Large Language Models (LLM) and advanced AI agents. This role is pivotal in enabling groundbreaking research in machine learning for scientific discovery, particularly in the realm of material science and battery technology.
- Harness internal expertise and collaborate with external research labs to advance scientific ML.
- Work will be incorporated directly into our groundbreaking Deep Space multi-agent system for battery technology discovery.
- This position can be remote.
Essential Duties and Responsibilities:
- Research & Development
- Lead cutting-edge research in machine learning for scientific discovery, with a focus on (multimodal) Large Language Models and their application (including AI agents) in battery and material discovery.
- Conduct groundbreaking research on integrating domain-specific data (including literature and internal documents) into LLM training and inference.
- Investigate the mechanisms through which LLMs approach problem-solving, planning, and solution generation, particularly in the context of basic battery design questions.
- Model Optimization & Implementation
- Troubleshoot and optimize the training process of large language models, addressing complexities and challenges related to data quality, model architecture, and computational efficiency.
- Implement innovative solutions to enhance model performance and scalability.
- Collaborate closely with a multidisciplinary team to integrate findings into practical AI solutions that contribute to the discovery of new battery materials and the advancement of lithium battery technology.
- Collaboration & Communication
- Contribute to academic and industry discussions by publishing research findings in top-tier journals and presenting at conferences.
- Engage in machine learning research aimed at addressing battery design challenges and enhancing system ability to interpret data-driven science efficiently.
- The ability to communicate complex concepts clearly and effectively to both technical and non-technical team members.
Education and/or Experience:
- MS or PhD in Computer Science, Statistics, Computational Neuroscience, Cognitive Science or a related field, or equivalent practical experience.
- Strong foundational knowledge and practical experience in Machine Learning, Deep Learning, and Large Language Models.
- Proficiency in programming languages relevant to machine learning, with a strong preference for Python.
- Experience with deep learning frameworks such as PyTorch or TensorFlow.
- Proficiency in utilizing causal graphs for AI research and application.
- A solid track record of innovative research, preferably with published work in relevant areas.
- Excellent problem-solving abilities and a passion for tackling complex technical challenges.
Preferred Qualifications:
- Experience with AI applications in material science or battery technology.
- Familiarity with the latest trends and methodologies in AI research, including algorithms such as GRPO.