Fundamental AI Research Scientist, Physical World Models, FAIR

Meta is seeking a Research Scientist to join the Fundamental AI Research (FAIR) team, one of the top industrial AI research organizations in the world.

Come join FAIR's efforts to build world models, learning to understand and make predictions about the physical world, especially from video, and develop efficient algorithms for world model-based planning and control.Our team is driving an ambitious agenda to train and use world models for embodied and wearable agents.

We innovate across related topics including self-supervised learning from video (e.g., joint-embedding predictive architectures/JEPAs), predictive models, model-based reinforcement learning and model-predictive control.

We accomplish this by advancing research across the stack, including data curation, training large-scale state-of-the-art models, and designing robust benchmarks.

We are looking for Research Scientists who share our passion for building efficient, scalable, and robust models of the world that will be part of the next paradigm in AI models.

Responsibilities:

Fundamental AI Research Scientist, Physical World Models, FAIR Responsibilities:

Leading, collaborating, and executing on research that pushes forward the state of the art in artificial intelligence

Performing research that enables learning the semantics of data (images, video, text, audio, and other modalities).

Working towards long-term research goals, while identifying immediate milestones.

Influencing progress of relevant research communities by producing publications.

Collaborating with scientists and engineers in a large cross-functional team

Open source high quality code and produce reproducible research

Qualification and experience:

Minimum Qualifications:

First-authored publications at peer-reviewed conferences, such as ICML, NeuRIPS, ICLR, CVPR, ICCV, CoRL, or similar

Research background in machine learning, artificial intelligence, computational statistics, applied mathematics, or related areas.

Experience coding software and executing complex experiments

Experience with Python and PyTorch

Preferred:

Preferred Qualifications:

Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as publications at leading workshops, journals or conferences in Machine Learning (NeurIPS, ICML, ICLR), Robotics (ICRA, IROS, RSS, CoRL), and Computer Vision (CVPR, ICCV, ECCV).

Experience collaborating in a team environment on research projects

Experience with manipulating and analyzing complex, large scale, high-dimensionality data from varying sources.

Experience building systems based on machine learning and/or deep learning methods.

Experience working and communicating cross functionally in a team environment.

Experience solving complex problems and comparing alternative solutions, tradeoffs, and different perspectives to determine a path forward.