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Artificial Intelligence could be one of humanity’s most useful inventions. At Google DeepMind, we’re a team of scientists, engineers, machine learning experts and more, working together to advance the state of the art in artificial intelligence. We use our technologies for widespread public benefit and scientific discovery, and collaborate with others on critical challenges, ensuring safety and ethics are the highest priority.

The training infrastructure team at Google DeepMind Robotics plays a critical role in our mission to realise general intelligence on physical robotic platforms. We develop and maintain the robust and scalable training pipelines that turn large-scale robotics data into high-quality perception and control agents. By enabling researchers to iterate quickly and efficiently, we accelerate the pace of innovation in robotics research.

We're looking for a research engineer to join our team and contribute to building the future of robot learning. In this role, you will work closely with research scientists, research engineers, software engineers, and stakeholders across DeepMind to develop and optimise the infrastructure that powers our cutting-edge robotics research. You will be involved in all aspects of the training pipeline, from data acquisition and augmentation to model training and evaluation.

• Collaborate with colleagues to design, build, and debug new model and agent architectures.
• Conduct experiments to analyse the effect of different architecture choices, hyperparameters, and dataset weights on model performance.
• Deploy and evaluate trained agents on real robots.
• Build and curate new robotics datasets for training, ensuring data quality and diversity.
• Investigate and diagnose performance regressions in the training pipeline, identifying and resolving bottlenecks.
• Balance long-term engineering goals with the need to support short-term research experimentation.

In order to set you up for success as a Research Engineer at Google DeepMind,  we look for the following skills and experience:

• BSc, MSc, or PhD/DPhil degree in computer science, mathematics, applied statistics, machine learning, or a related field, or equivalent industry experience.
• Proven knowledge of Python.
• Solid understanding of machine learning and statistics.

In addition, the following would be an advantage: 

• Hands-on experience with robotics systems and applications.
• Experience with ML frameworks (e.g., JAX).
• Experience handling large-scale datasets and training large models.
• Debugging and performance analysis skills.