Research Scientist Intern, FAIR Chemistry

Job description

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. Our internships are twelve (12) to sixteen (16), or twenty-four (24) weeks long and we have various start dates throughout the year.

Responsibilities

Advancing the state-of-the-art in AI for generative atomic world models. Developing datasets to train and test AI models for Chemistry. Developing, training, and scaling AI models for Chemistry in PyTorch. Running large-scale chemistry simulations. Developing processes to feedback experimental results into chemistry models. Efficiency optimization of classic and ML based chemistry software. Writing research papers and associated open source data and code releases.

Qualifications

Currently has or is in the process of obtaining a Ph.D. degree in Machine Learning, Chemistry, Chemical Engineering, Materials Science, Physics, Artificial Intelligence, or relevant technical field Experience applying artificial intelligence to a scientific domain such as computational photonics, computational design, computational chemistry, etc 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 other related language Experience with deep learning frameworks such as Pytorch, Jax, or Tensorflow

• Must obtain work authorization in country of employment at the time of hire and maintain ongoing work authorization during employment Intent to return to the degree program after the completion of the internship/co-op
• Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as 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-dimensionality data from varying sources Experience in utilizing theoretical and empirical research to solve problems Experience doing optimization based on machine learning and/or deep learning methods
• Demonstrated software engineering experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g.

GitHub) competitions, or widely used contributions in open source repositories (e.g. GitHub). Experience working and communicating cross functionally in a team environment