Machine Learning Scientist - AI Explainability

March 27

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SES AI Corp

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Renewable Energy, UAV, Electrical Manufacturing, Wearables, Electric Vehicles, Batteries, Li-Ion, Li-Metal, Drones, HAPS, Electrochemistry, Smartphones • eVTOL • EV

201 - 500

Description

• Conduct groundbreaking research on the explainability of large language models and their reasoning processes, drawing parallels with neuroscience to understand the 'thought processes' of AI systems. • Investigate the mechanisms through which LLMs approach problem-solving, planning, and solution generation, particularly in the context of basic battery design questions. • Apply advanced techniques such as causal graphs and neuroscience-inspired AI methodologies to dissect and enhance the reasoning capabilities of LLMs, with the aim of improving accuracy and reducing instances of hallucination. • 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-metal battery technology. • Contribute to academic and industry discussions by publishing research findings in top-tier journals and presenting at conferences.

Requirements

• 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 utilizing causal graphs for AI research and application. • Demonstrated experience in AI explainability, with a focus on mechanistic interpretation and representation engineering. • A solid track record of innovative research, preferably with published work in relevant areas. • 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. • Excellent problem-solving abilities and a passion for tackling complex technical challenges. • The ability to communicate complex concepts clearly and effectively to both technical and non-technical team members.

Benefits

• Remote position