Research Scientist, Robotics, Deepmind

Google Google · Big Tech · Cambridge, MA +1

Research Scientist at Google DeepMind Robotics focused on integrating AI into physical agents. The role involves building foundation models like vision-language-action (VLA) models for direct robotic control, enabling robots to perceive, plan, think, use tools, and act. Key areas of research include agentic reasoning, real-world understanding, action generalization, human-robot interaction, dexterity, whole-body control, and continual learning, with a goal to deploy these innovations at scale.

What you'd actually do

  1. Design, implement, train, and evaluate large models and algorithms for robotic agents to make breakthroughs and unlock new robot capabilities.
  2. Write software to implement research ideas and iterate quickly.
  3. Participate in a wide variety of research, including learning from simulation, reinforcement learning, learning from demonstrations, vision-language-action (VLA) models, transformers, video generation, robot control, humanoid robots, and more.
  4. Work effectively with a large collaborative team with changing agendas to meet ambitious research goals.
  5. Generate creative ideas, set up experiments, and test hypotheses to report and present research findings clearly and efficiently both internally and externally.

Skills

Required

  • PhD degree in Computer Science, a related field, or equivalent practical experience.
  • Experience contributing to research communities or efforts, including publishing papers at conferences (e.g., NeurIPS, CoRL, ICML, ICLR).
  • Experience working with simulators and robots.

Nice to have

  • Experience training neural networks using large datasets or simulation to improve real robot behavior.
  • Experience in robot manipulation.
  • Experience with Python programming.

What the JD emphasized

  • PhD degree in Computer Science, a related field, or equivalent practical experience.
  • Experience contributing to research communities or efforts, including publishing papers at conferences (e.g., NeurIPS, CoRL, ICML, ICLR).

Other signals

  • foundation models
  • physical agents
  • robot control
  • agentic reasoning
  • real-world understanding
  • action generalization
  • human-robot interaction
  • dexterity
  • whole-body control
  • continual learning