AI Research Scientist, Fair Chemistry

Meta · Big Tech · New York, NY +1

Research Scientist role focused on developing AI-based methods for chemistry and materials science applications, including energy sustainability, drug discovery, and new materials. The role involves fundamental methods development for predictive and generative AI models, working with interdisciplinary teams, and publishing research.

What you'd actually do

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

Skills

Required

PhD in AI, computer science, data science, science/engineering, or related technical fields
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
Experience in applying AI systems to solve science and engineering problems

Nice to have

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
Experience in molecular simulations, long timescale dynamics, or generative models
Open source high quality code and produce reproducible research

What the JD emphasized

First-authored or last-authored publications at peer-reviewed conferences
Experience in applying AI systems to solve science and engineering problems
Experience in molecular simulations, long timescale dynamics, or generative models

Other signals

AI for science
fundamental methods development
predictive and generative AI models
state-of-the-art in AI for chemistry and materials science

Read full 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