Research Scientist, World Modelling
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
Preferred Qualifications
• 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
