At Google DeepMind, we value diversity of experience, knowledge, backgrounds and perspectives and harness these qualities to create extraordinary impact. We are committed to equal employment opportunity regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy, or related condition (including breastfeeding) or any other basis as protected by applicable law. If you have a disability or additional need that requires accommodation, please do not hesitate to let us know.
Snapshot
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.
About Us
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.
The Role
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.
Key responsibilities:
- 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.
About You
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.
Similar Jobs
What We Do
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.
Our long term aim is to solve intelligence, developing more general and capable problem-solving systems, known as artificial general intelligence (AGI).
Guided by safety and ethics, this invention could help society find answers to some of the world’s most pressing and fundamental scientific challenges.
We have a track record of breakthroughs in fundamental AI research, published in journals like Nature, Science, and more.Our programs have learned to diagnose eye diseases as effectively as the world’s top doctors, to save 30% of the energy used to keep data centres cool, and to predict the complex 3D shapes of proteins - which could one day transform how drugs are invented.
