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.
Requirements
- 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
- Intent to return to the degree program after the completion of the internship/co-op
- Experience solving analytical problems using quantitative approaches
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.
Other
- Currently has or is in the process of obtaining a Ph.D. degree in Machine Learning, Chemistry, Chemical Engineering, Physics, Artificial Intelligence, or relevant technical field
- Must obtain work authorization in country of employment at the time of hire and maintain ongoing work authorization during employment
- 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
- Ability to manipulate and analyze complex, large scale, high-dimensionality data from varying sources
- Experience in utilizing theoretical and empirical research to solve problems
