Machine Learning Scientist, LLM

About Us

SES AI is at the forefront of revolutionizing lithium-metal battery creation with our groundbreaking approach that integrates cutting-edge machine learning techniques into our research and development processes. Our mission is to lead the next wave of scientific discovery in material science, powered by advanced AI technologies with a dedication to AI for Science.

To learn more about SES, please visit: www.ses.ai

Position Scope

The SES AI Prometheus team (AI Research) is seeking exceptional Machine Learning Scientists to spearhead the development of our Large Language Models (LLM) and AI agents. By harnessing our internal expertise and collaboration with strong academic labs, this role is pivotal in enabling groundbreaking research in machine learning for scientific discovery, particularly in the realm of material science and battery technology.

As a Large Language Model Scientist at SES AI, you will be instrumental in building and deploying large-scale, distributed (multimodal) large language models, AI agents and relevant machine learning algorithms and systems. These innovations are key to transforming the material science discovery process, enabling the machine to not only absorb knowledge from extensive literature in the battery and material science fields but also to think and innovate like a scientist.

This role demands a blend of passion for technical problem-solving and a proven track record in delivering innovative machine learning solutions. Your tasks will include the continuous pretraining of large language models using material science literature and textbooks, alongside supervised fine-tuning with battery data labeled by human scientists for reasoning. Subsequently, you will create AI agents based on these developed LLMs or multimodal LLMs (MLLMs), fostering machine-driven innovation in discovering novel materials for our lithium-metal battery designs.

You will be required to collaborate with strong academic labs, engaging in machine learning research aimed at addressing our battery design challenges and enhancing our systems' ability to understand and interpret data-driven science efficiently. Your contributions will be instrumental in enhancing our ability to analyze experimental data and intuitively achieve groundbreaking advancements in battery technology.

This is a remote position.

Responsibilities

• Lead cutting-edge research in machine learning for scientific discovery, with a focus on (multimodal) large language models and their application (including AI agent) in battery and material discovery.
• Troubleshoot and optimize the training process of large language models, addressing the complexities and challenges inherent to training such sophisticated systems. This includes identifying and resolving issues related to data quality, model architecture, and computational efficiency, as well as implementing innovative solutions to enhance model performance and scalability.
• Design, develop, and deploy robust machine learning models in production environments, ensuring their scalability and reliability in collaboration with other computational teams.
• Foster collaboration with cross-functional teams across our AI team and other teams at SES, aiming to solve complex problems in battery and material sciences.
• Engage with external scientific partners, including top academic institutions and industry research groups, to drive innovation and research. Endeavor to publish research findings in top-tier machine learning conferences and journals.

Qualifications

• MS or PhD in Computer Science, Statistics, or a related field, or equivalent practical experience.
• At least 5 years of strong academic and industry experience in machine learning and natural language processing, with a preference for candidates who have focused on large language models and AI agents.
• Have a distinguished history of contributing to the field through publications in leading machine learning conferences and journals, such as ICLR, NeurIPS, ACL, ICML, CVPR, and Nature Machine Intelligence.
• Proficient in programming languages relevant to machine learning, with a strong preference for Python.
• Experience with deep learning frameworks such as PyTorch or Tensorflow etc.
• An extensive track record of delivering innovative solutions in machine learning.
• Exceptional communication skills, capable of conveying complex technical concepts to a broad audience, including both technical and non-technical stakeholders.
• Demonstrated ability to collaborate effectively with external scientific partners.