The role
From Google's own posting.
As a part of this role, you will lead our efforts in developing novel algorithms and models for embodied AI and Artificial General Intelligence as we are looking for creative thinkers with strong algorithm-design and programming skills that want to build the next-generation of AI agents for and grounded in the physical world. You will work in collaborative teams to invent algorithms and innovate on large foundation models such as Gemini Robotics. You will design prototype applications and work with real robots inside and outside the lab to address real-world use cases, strong algorithmic background in scalable machine learning (e.g. reinforcement learning/imitation learning; multimodal foundation models) and experience with real robots/robot simulation and large-scale training setups are valued.
In this role, you will have experience with approaches for mid-/post-training for robotics foundation models, including reinforcement learning and related techniques that enable agents to self-improve and achieve mastery, including experience with the use of Reinforcement Learning (RL) to train robot policies in simulation and in the real world. You will have experience with such techniques in the context of general multimodal frontier models (e.g. LLMs, VLMs, video models) and agentic systems are also highly valued.
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, train, and evaluate scalable algorithms for robotic agents, with a specific focus on post-training, self-improvement, and mastery.
Leverage your experience to participate in a wide variety of other research themes in the context of robotics foundation models and physical agents (e.g. VLAs, WAMs, imitation learning, simulation-based learning, whole body control, dexterity, and more).
Develop scalable research pipelines and write robust software to test hypotheses quickly and conduct research at pace.
Collaborate within a fast-paced team to execute ambitious research goals.
Generate creative ideas, set up experiments and test hypotheses, reporting and presenting research findings clearly and efficiently both internally and externally.
Minimum qualifications:
PhD degree in a technical field or equivalent practical experience.
2 years of experiencein reinforcement learning or other techniques for mid-/post-training of foundation models and self-improvement in robotics, and other areas such as multimodal generative modeling, training and inference, vision-language/video, or other multimodal models.
Preferred qualifications:
Experience working with simulators and real-world robots, esp. dexterous manipulation including multi-fingered hands or whole body control, as well as multimodal sensing (e.g. tactile).
Experience deploying the above models and algorithms on real-world systems.
Experience implementing large-scale systems and working with large real world data.
A passion for bringing research from the lab to real-world robotic systems.
PhD degree in a technical field or equivalent practical experience.
2 years of experiencein reinforcement learning or other techniques for mid-/post-training of foundation models and self-improvement in robotics, and other areas such as multimodal generative modeling, training and inference, vision-language/video, or other multimodal models.