Robotic Controls Researcher

Meta Meta · Big Tech · Redmond, WA

Robotic Controls Researcher at Meta Reality Lab focusing on developing control algorithms for robotics platforms, including model predictive control and generative AI models. The role involves data collection and evaluation for training autonomous control policies.

What you'd actually do

  1. Conducting collaborative research on developing control algorithms for a wide range of robotics platforms
  2. Development of model predictive control approaches mapping robot observations and target references to low-level actuation control signals
  3. Development of robotic data collection sets and evaluations

Skills

Required

  • Ph.D. in Mechanical Engineering, Electrical Engineering, Control Systems Engineering, Computer Science, or relevant degree and 5+ years experience in robotic control systems
  • 2+ years experience with both traditional reflexive controllers (PID, LQR, OSC) and modern predictive controllers (MPCs)
  • Experience with physical systems, including interfacing with novel sensors and actuators
  • Experience working with robot manipulation
  • Experience with robotic data collection for training autonomous control policy models

Nice to have

  • Bachelor's degree in Mechanical Engineering, Electrical Engineering, Control Systems Engineering, Computer Science, or in a relevant technical field, or equivalent practical experience
  • Experience with both traditional reflexive controllers (PID, LQR, OSC) and modern predictive controllers (MPCs)
  • Experience with generative AI models such as transformers, LLMs, VLMs, VLAs, and diffusion models
  • A track record of research contributions with your work published in top conferences and journals such as Robotics (RSS, ICRA, IROS, CoRL, T-RO, IJRR), Machine Learning (NeurIPS, ICML, ICLR, AAAI, JMLR), and Computer Vision (CVPR, ICCV, ECCV, TPAMI)

What the JD emphasized

  • track record of research contributions with your work published in top conferences and journals

Other signals

  • robot embodiments
  • modern control policies
  • robot dexterous manipulation
  • generative AI models
  • robotic data collection
  • training autonomous control policy models