About Google
Learn more about Google. Explore our innovative AI products and services, and how we. Backed by Kleiner Perkins.
About the role
As an organization, Google maintains a portfolio of research projects driven by fundamental research, new product innovation, product contribution and infrastructure goals, while providing individuals and teams the freedom to emphasize specific types of work. As a Research Scientist, you'll setup large-scale tests and deploy promising ideas quickly and broadly, managing deadlines and deliverables while applying the latest theories to develop new and improved products, processes, or technologies. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- PhD in Computer Science, a related technical field, or equivalent practical experience
- Experience working with robot policy learning (e.g., imitation learning, RL) across simulators and real-world robotic platforms (e.g., dexterous manipulation, multimodal sensing)
- Experience working with simulators and robots
- Experience contributing to research communities or efforts, including publishing papers at conferences (e.g., NeurIPS, CoRL, ICML, ICLR)
More about this role
- Develop data quality, filtering, and labeling methodologies, and optimize multimodal data representations and mixtures to upstream physical and action knowledge into Gemini pre-training.
- Design and run pre-training and policy training ablations, improve training recipes, and systematically hill-climb physical reasoning and action performance against benchmarks.
- Build and maintain the research software, evaluation tools, and lightweight post-training workflows required to iterate on research ideas quickly and verify upstream data quality through offline and closed-loop robot policy evaluations.
- Contribute to our wider research agenda—including vision-language-action (VLA) modeling, world-action models, reinforcement learning, and simulation—and communicate findings clearly in a fast-paced, collaborative team environment.
- PhD in Computer Science, a related technical field, or equivalent practical experience.
- Experience working with robot policy learning (e.g., imitation learning, RL) across simulators and real-world robotic platforms (e.g., dexterous manipulation, multimodal sensing).
- Experience working with simulators and robots.
- Experience contributing to research...
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