At a Glance
- Tasks: Design and evaluate algorithms for robotic agents, focusing on self-improvement and mastery.
- Company: Join DeepMind, a leader in AI research and innovation.
- Benefits: Competitive salary, cutting-edge projects, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on creative problem-solving and scientific communication.
- Why this job: Make a real impact on robotics and AI, shaping the future of intelligent agents.
- Qualifications: PhD or equivalent experience in reinforcement learning and robotics.
The predicted salary is between 59400 - 72600 £ per year.
In this role you design, train, and evaluate scalable algorithms for robotic agents, with emphasis on post-training self-improvement and mastery. You will develop research pipelines and robust software to test hypotheses quickly, collaborating with a fast-paced team to pursue ambitious robotics goals. You’ll generate novel ideas, set up experiments, and clearly report findings to both internal and external audiences. You will contribute to embodied AI initiatives and help advance large foundation models applied to real-world robots, offering a meaningful impact on how intelligent agents operate in physical environments.
Responsibilities
- Design, train and evaluate scalable algorithms for robotic agents
- Develop scalable research pipelines and robust software to test hypotheses
- Collaborate within a fast-paced team to achieve research goals
- Generate ideas, set up experiments and test hypotheses
- Present research findings clearly internally and externally
- Lead development of novel algorithms and models for embodied AI and foundation models
- Prototype applications and work with real robots to address real-world use cases
- Apply RL/IM techniques for mid-/post-training and self-improvement in robotics
- Bridge lab research with real-world robotic deployment and large-scale systems
Key requirements
- PhD in a technical field or equivalent practical experience
- 2 years of experience in reinforcement learning or other post-training techniques for foundation models and self-improvement in robotics
- Experience with simulators and real-world robots, esp. dexterous manipulation and whole body control
- Experience deploying models and algorithms on real-world systems
- Experience implementing large-scale systems and working with large real-world data
- Strong algorithmic background in scalable machine learning (RL/imitation learning; multimodal models)
- Passion for translating research from lab to real-world robotic systems
- Collaboration in multi-disciplinary teams
- Creative problem solving
- Clear scientific communication
- Reinforcement learning
- Imitation learning
- Multimodal foundation models (e.g., LLMs, VLMs, video models)
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