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. In this role, you will help build world models that learn 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 agents. We innovate across related topics including self-supervised learning from video, 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
- Lead, collaborate, and execute on research that pushes forward the state of the art in world modelling and artificial intelligence
- Perform research that enables learning the semantics of data across modalities including images, video, text, and audio
- Work towards long-term research goals while identifying immediate milestones
- Develop and evaluate novel architectures and training methods for learning predictive models of visual, physical, or multimodal environments
- Explore applications of world models to planning, prediction, control, and decision-making for embodied agents
- Influence progress of relevant research communities by producing publications at peer-reviewed venues
- Collaborate with scientists and engineers in a large cross-functional team
- Open source high quality code and produce reproducible research
- Support recruiting efforts by engaging with potential candidates and sharing insights about world modelling research at Meta
Minimum Qualifications
- Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
- Currently has, or is in the process of obtaining, a PhD degree in AI, computer science, data science, robotics, or related technical fields
- First-authored publications at peer-reviewed conferences such as ICML, NeurIPS, ICLR, CVPR, ICCV, CoRL, RSS, or ICRA, or similar
- Research background in machine learning, artificial intelligence, robot learning, computational statistics, applied mathematics, or related areas
- Experience coding software and executing complex experiments
- Experience with Python and PyTorch Experience solving complex problems and comparing alternative solutions, tradeoffs, and different perspectives to determine a path forward
- Track record of achieving significant results as demonstrated by grants, fellowships, patents, or publications at leading conferences in Machine Learning (NeurIPS, ICML, ICLR), Robotics (ICRA, IROS, RSS, CoRL), or Computer Vision (CVPR, ICCV, ECCV)
- Experience with self-supervised learning from video, predictive models, model-based reinforcement learning, or model-predictive control
- Experience building systems based on machine learning or deep learning methods
- Experience manipulating and analyzing complex, large-scale, high-dimensionality data from varying sources
- Experience collaborating in a team environment on research projects