The role
From Google's own posting.
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.
For this position, we are especially looking for individuals who have experience with incorporating human video action data and / or data captured with wearable devices (eg UMI, gloves) with other data sources to push the boundaries of dexterity and generalization for robotics foundation models. This includes the design and use of the capture devices, the datasets, as well as algorithmic and modeling approaches to leverage such data.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 varied learning opportunities and career pathways for those driven to achieve exceptional results through collective effort.
Design, implement, train and evaluate large models and algorithms for robotic agents, making breakthroughs and unlocking new robot capabilities.
Write software to implement research ideas and iterate quickly, working effectively with a large collaborative team with fast-paced agendas to meet ambitious research goals.
Develop methodologies and design and conduct experiments for incorporating scalable data sources, especially human data with or without capture devices into our robotics foundation models.
Leverage broader expertise to participate in a wide variety of research, including learning from simulation, reinforcement learning, learning from demonstrations, vision-language-action models, transformers, video generation, robot control, humanoid robots and more.
Report and present research findings clearly and efficiently both internally and externally.
Minimum qualifications:
PhD in Computer Science, a related field, or equivalent practical experience.
2 years of experience with reinforcement learning and imitation learning.
Experience with vision, vision-language, video, and other multimodal models.
Experience with multimodal generative modeling, training and inference.
Preferred qualifications:
Experience with capture methodologies, dataset design, experimentation, and incorporation of captured human action data into VLAs or WAMs.
Experience working with simulators and real-world robots, esp. dexterous manipulation, as well as multimodal sensing (e.g. tactile, forces).
A passion for bringing research from the lab to real-world robotic systems.
PhD in Computer Science, a related field, or equivalent practical experience.
2 years of experience with reinforcement learning and imitation learning.
Experience with vision, vision-language, video, and other multimodal models.
Experience with multimodal generative modeling, training and inference.