AI Research Scientist, Fundamental - (FAIR) Embodied AI / Robotics

Meta is seeking a Research Scientist to join Fundamental AI Research (FAIR), a research organization focused on making significant progress in AI.

Individuals in this role are expected to be recognized experts in identified research areas such as artificial intelligence, machine learning, robotics, and embodied AI, particularly including areas such as deep learning, world models, reinforcement learning, robot control and planning, imitation learning and representation learning.

You should have a keen interest in producing new, open science to make embodied agents more intelligent.

Responsibilities:

AI Research Scientist, Fundamental - (FAIR) Embodied AI / Robotics Responsibilities:

Perform fundamental and applied research to push the scientific and technological frontiers of embodied artificial intelligence.

Investigate world modeling paradigms that can deliver a spectrum of embodied behaviors on real robots.

Invent/improve novel data-driven paradigms for embodied intelligence leveraging a variety of modalities (images, video, text, audio, tactile, etc).

Qualification and experience:

Minimum Qualifications:

1+ years of industry or PostDoctoral experience in relevant robotics related research areas, such as: action-conditioned world models, task and motion planning, robotic control, manipulation, navigation, or generally embodied AI

Experience in deep learning frameworks (such as pytorch, tensorflow), C, C++, Python.

Experience in robotics frameworks like ROS, along with experience working with robot simulations and hardware.

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).

Demonstrated research and software engineering experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub).

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

Experience working and communicating cross functionally in a team environment.

Research experience in machine learning, computer vision, representation learning, optimization, statistics, applied mathematics, or data science.