Research Scientist, Robotics, Deepmind

Google Google · Big Tech · Cambridge, MA +1

Research Scientist at Google DeepMind Robotics focused on building foundation models, specifically vision-language-action (VLA) models, for physical agents to enable robots to perceive, plan, think, use tools, and act. The role involves advancing general-purpose robotics through research in areas like agentic reasoning, real-world understanding, and human-robot interaction, with the goal of deploying 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 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

  • 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
  • vision-language-action (VLA) models