In this role, you'll work with world-class researchers at FAIR on fundamental and exploratory research. Our current projects include new datasets, models, and capabilities to accelerate chemistry and materials science. Our organization is motivated by producing new science to understand intelligence and technology towards achieving advanced machine intelligence. In this specific role, you will develop and enhance capabilities for modeling complex battery and electrolyte chemistries.
Responsibilities
Provide technical leadership for a scientific direction in AI for science.
• Perform research that advances the state-of-the-art in AI for chemistry and materials science.
• Work closely with internal and external partners to realize impact of methods advances.
• Work towards long-term goals, while identifying intermediate milestones.
• Influence progress of relevant research communities by producing publications.
• Open source high quality code and produce reproducible research.
• Enhance the capabilities of FAIR chemistry models in complex chemical environments like electrolytes or strong applied fields.
Minimum Qualifications
• First-authored or last-authored publications at peer-reviewed conferences, such as ICML, NeurIPS, ICLR, and other similar venues, or domain scientific journals
• Master's Degree or PhD in AI, computer science, data science, science/engineering, or related technical fields
• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• Research background in machine learning, artificial intelligence, computational statistics, applied mathematics, AI for science, or related areas
• Experience in developing and debugging in Python or similar programming languages
• Currently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta
Preferred Qualifications
• Experience collaborating in a team environment on research projects
• Experience in molecular simulations for batteries, complex electrolytes, or simulation techniques like grand canonical density functional theory
• Research and engineering experience demonstrated via publications, grants, fellowships, patents, open source code
• Experience in applying AI systems to solve science and engineering problems
