Research Scientist, Safety and Alignment for Humanoid Robotics

New York City, New York, United StatesOn-site

Responsibilities

Research Scientists work on a diverse and stimulating range of projects including: inventing algorithms and prototype applications, working with real robots in the lab and outside the lab tackling real world use cases.
Research Scientists work in large collaborative teams to make cutting edge breakthroughs to advance Robotics AI.
Design, implement, train and evaluate large models and algorithms for humanoid robots.
Make breakthroughs and unlock new humanoid safety capabilities. Areas of focus include:
Write software to implement research ideas and iterate quickly.
Generate creative ideas, set up experiments and test hypotheses. Report and present research findings clearly and efficiently both internally and externally. About You
Research track record in one or more of the following topics: Humanoid Whole Body Control, Vision Language Action models; Motion Planning, Force Control, AI Safety, Diffusion Policies, World Models, Imitation Learning and Reinforcement Learning, Sim2Real Transfer, Alignment Techniques

Artificial Intelligence could be one of humanity’s most useful inventions.
At Google DeepMind, we’re a team of scientists, engineers, machine learning experts and more, working together to advance the state of the art in artificial intelligence.
We are looking for Research Scientists to join the Robotics team whose mission is to “Build embodied AI responsibly to benefit people in the physical world.
We believe large foundation models will transform general purpose Robotics like with our Gemini Robotics models.
This role will involve working with a diverse multi-functional team that builds the future of robotics.
Algorithmic and model development to improve a robot agent's understanding of it’s own embodiment and VLA capabilities;
In order to set you up for success as a Research Scientist at Google DeepMind, we look for the following skills and experience:
PhD in a technical field or equivalent practical experience.
Knowledge of the latest in large machine learning research. •
Experience working with real-world robots
Expertise in using large datasets with deep neural networks to make real robots useful