Research Scientist Intern - FAIR Chemistry

Meta is seeking Research Interns to join the 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. Internships are twelve (12) to sixteen (16), or twenty-four (24) weeks long, with various start dates throughout the year. ## Responsibilities - Advance the state of the art in AI for generative atomic world models. - Develop datasets to train and test AI models for Chemistry. - Develop, train, and scale AI models for Chemistry in PyTorch. - Run large-scale chemistry simulations. - Develop processes to feed experimental results back into chemistry models. - Optimize the efficiency of classic and ML-based chemistry software. - Write research papers and associated open-source data and code releases. ## Minimum Qualifications - Currently have or be in the process of obtaining a Ph.D. degree in Machine Learning, Chemistry, Chemical Engineering, Materials Science, Physics, Artificial Intelligence, or a relevant technical field. - Experience applying artificial intelligence to a scientific domain such as computational photonics, computational design, or computational chemistry. - Experience devising data-driven models and real-system experiments and design implementation for AI design and optimization. - Experience with scalable machine learning systems, resource-efficient AI data and algorithm scaling, or neural network architectures. - Experience with Python, C++, C, Julia, or another related language. - Experience with deep learning frameworks such as PyTorch, JAX, or TensorFlow. - Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment. ## Preferred Qualifications - Intent to return to the degree program after completing the internship or co-op. - Proven track record of achieving significant results, as demonstrated by grants, fellowships, patents, or first-authored publications at leading workshops or conferences such as NeurIPS, ICML, CVPR, ICCV, ICLR, or similar. - Experience solving analytical problems using quantitative approaches. - Ability to manipulate and analyze complex, large-scale, high-dimensional data from varying sources. - Experience using theoretical and empirical research to solve problems. - Experience doing optimization based on machine learning and/or deep learning methods. - Demonstrated software engineering experience through an internship, work experience, coding competitions, or widely used contributions in open-source repositories such as GitHub. - Experience working and communicating cross-functionally in a team environment.