Research Scientist, Robotics Pre-Training and Data Quality, DeepMind
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MINIMUM QUALIFICATIONS:
- PhD in a technical field or equivalent practical experience.
- Experience with algorithmic architectures, data sources, and training and inference techniques for generative multimodal models, especially for robotics applications (e.g., VLAs, WAMs).
- Experience optimizing models and systems (e.g., performance tuning, experimentation, and debugging).
PREFERRED QUALIFICATIONS:
- Practical experience in "hill-climbing" model performance (e.g., iteratively improving training recipes and model benchmarks).
- Experience working with simulators and real-world robotic platforms (e.g., dexterous manipulation, multimodal sensing).
- Experience with benchmarking, different data sources, data labeling strategies, and data mixture optimization for robotics foundation models.
- A passion for bringing research from the lab to robust, real-world robotic systems.
- Proven ability to build and maintain the software tools for rapid iteration on research ideas.
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.
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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.
For this position, we are looking for individuals who are passionate about solving robotic problems in the real world and eager to get direct with foundation model training.
In this role, you will have experience with robot policy training using imitation learning and related techniques, alongside experience navigating the realities of pre-training. You should be enthusiastic about—and experience with—practical considerations such as data quality, mixture optimization, data labeling, systematic benchmarking, scaling ladders, performance hill-climbing. You will manage large volumes of multimodal data and optimizing data mixtures for training general, non-robotics frontier models (including LLMs, VLMs, image/video generation, and world models).
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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 learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.
RESPONSIBILITIES:
- Drive data quality, acquisition, and labeling strategies, and oversee mixture optimization for robotics foundation models.
- Coordinate training runs, improve training recipes, and systematically hill-climb model performance against benchmarks.
- Build and maintain the software tools and processes required to iterate on research ideas quickly and verify data quality through robot policy training.
- Contribute to our wider research agenda, including reinforcement learning, vision-language-action modeling, world-action models, and simulation, working in a fast-paced, collaborative team environment.
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