AI Research Scientist, Robotics

Responsibilities

Perform fundamental and applied research to push the scientific and technological frontiers of embodied artificial intelligence.
Invent/improve novel data-driven paradigms for robotics, leveraging a variety of modalities (images, video, text, audio, tactile, etc)
Investigate paradigms that can deliver a spectrum of embodied behaviors - from simulated characters to real robots, and from short-horizon, low-level to long-horizon, high-level intelligence
Develop algorithms based on state-of-the-art machine learning and neural network methodologies
Define, build and benchmark new functionalities needed for the next generation of AI
Conduct research towards long-term product goals while identifying intermediate milestones
Plan and execute novel research based on long-term objectives of the organization

Requirements

• 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 the field of Artificial Intelligence, Robotics, Computer Vision, Machine Learning, Language, a related field, or equivalent practical experience
  • Experience with any of the following research areas: robotics, motion planning, embodied AI, human-robot interaction, sim-to-real transfer, learning from demonstration, reinforcement learning, dexterous manipulation, digital agents, vision language models, computer vision, egocentric perception, and/or LLMs
  • Experience in relevant robotics related research areas, such as: VLM, robot learning, reinforcement learning, imitation learning, action-conditioned world models, task and motion planning, sim-to-real transfer robotic control, manipulation, navigation, or generally embodied AI
• Preferred Qualifications:
  • Experience solving complex problems and comparing alternative solutions, tradeoffs, and different perspectives to determine a path forward
  • Experience working with robot simulations and real-world hardware
  • Experience working and communicating cross functionally in a team environment
  • 2+ years of industry experience in relevant robotics related research areas, such as: robot learning, reinforcement learning, imitation learning, action-conditioned world models, task and motion planning, sim-to-real transfer robotic control, manipulation, navigation, or generally embodied AI
  • 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), Computer Vision (CVPR, ICCV, ECCV)
  • 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
  • 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 deep learning frameworks (such as PyTorch, Tensorflow) and Python

Qualifications

• 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 the field of Artificial Intelligence, Robotics, Computer Vision, Machine Learning, Language, a related field, or equivalent practical experience
  • Experience with any of the following research areas: robotics, motion planning, embodied AI, human-robot interaction, sim-to-real transfer, learning from demonstration, reinforcement learning, dexterous manipulation, digital agents, vision language models, computer vision, egocentric perception, and/or LLMs
  • Experience in relevant robotics related research areas, such as: VLM, robot learning, reinforcement learning, imitation learning, action-conditioned world models, task and motion planning, sim-to-real transfer robotic control, manipulation, navigation, or generally embodied AI
• Preferred Qualifications:
  • Experience solving complex problems and comparing alternative solutions, tradeoffs, and different perspectives to determine a path forward
  • Experience working with robot simulations and real-world hardware
  • Experience working and communicating cross functionally in a team environment
  • 2+ years of industry experience in relevant robotics related research areas, such as: robot learning, reinforcement learning, imitation learning, action-conditioned world models, task and motion planning, sim-to-real transfer robotic control, manipulation, navigation, or generally embodied AI
  • 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), Computer Vision (CVPR, ICCV, ECCV)
  • 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
  • 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 deep learning frameworks (such as PyTorch, Tensorflow) and Python

About Meta

Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today—beyond the constraints of screens, the limits of distance, and even the rules of physics.