Job Description

Meta is seeking Research Scientists to join the Fundamental AI Research (FAIR) Chemistry team. The Chemistry team develops AI-based methods to model the world at the atomic level. Chemistry and materials science applications include energy sustainability, drug discovery and new materials for display technologies. Researchers will drive fundamental methods development to enable predictive and generative AI models across these applications. They will work with an interdisciplinary team of domain scientists, AI experts, and software engineers, as well as academic and industrial partners.

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

Qualifications
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
* A PhD in AI, computer science, data science, science/engineering, or related technical fields
* First-authored or last-authored publications at peer-reviewed conferences, such as ICML, NeurIPS, ICLR, and other similar venues
* 4+ years experience holding an industry, postdoctoral, faculty, or government researcher position
* 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 Research and engineering experience demonstrated via publications, grants, fellowships, patents, open source code
* Experience collaborating in a team environment on research projects
* Experience in applying AI systems to solve science and engineering problems
* Experience in molecular simulations, long timescale dynamics, or generative models