The posting
Minimum qualifications:
- 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).
Preferred qualifications:
- Experience with systematic benchmarking, multimodal data sources, data labeling strategies, and data mixture optimization for frontier or robotics foundation models.
- Practical experience in "hill-climbing" model performance (e.g., iteratively improving data curation, training recipes, and model benchmarks).
- Proven ability to build and maintain clean research software and tools for rapid iteration on research ideas.
- A passion for bringing foundation model research from the lab to robust, real-world robotic systems.
About the job
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. From creating experiments and prototyping implementations to designing new architectures, our research scientists work on real-world problems that span the breadth of computer science, such as machine (and deep) learning, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more.
As a Research Scientist, you'll also actively contribute to the wider research community by sharing and publishing your findings, with ideas inspired by internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.
Our mission is to combine Gemini foundation models with physical intelligence so general-purpose robots can understand and act reliably in the real world. Critical to achieving this goal is obtaining high-quality training data and effectively integrating it into the Gemini training process. Our team's research develops data quality insights on how to best extract physical and action knowledge from robot-related datasets, and incorporates these insights into Gemini pre-training through model training and upstreaming. By pairing data ablations with fast closed-loop robot evaluations, you will shape how physical reasoning and action capabilities emerge in next-generation Gemini models and deploy onto real-world robots.
Artificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.
We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $147000 - $210000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- 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.
