Research Scientist, Continual Learning

At 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, maternity 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

Research Scientists at DeepMind lead our efforts in developing novel algorithmic architecture towards the end goal of solving and building Artificial General Intelligence.

Having pioneered research in the world's leading academic and industrial labs in PhDs, post-docs or professorships, Research Scientists join DeepMind to work collaboratively within and across Research fields. They develop solutions to fundamental questions in machine learning, computational neuroscience and AI.

Drawing on expertise from a variety of disciplines including deep learning, reinforcement learning, computer vision, language, neuroscience, safety, control, robotics and multi-agent, our Research Scientists are at the forefront of groundbreaking research.

The Continual Learning Team, led by Marc’Aurelio Ranzato, seeks to build learning systems that can efficiently and effectively acquire new skills over time. We believe that intelligence is not just about how well a system can learn a new task, but also how few resources it uses and how quickly it can adapt. A critical element towards this goal is the ability to use knowledge acquired while performing related tasks and to accrue information over time. We draw upon a range of technical fields including deep learning, reinforcement learning, transfer learning, meta-learning, auto-ML, compositional learning and large-scale learning, valuing diversity of perspective to bring these together towards a single objective.

Our team’s approach is focused and creative, with a highly supportive and collaborative culture. Past projects that current team members contributed to include the CTrL benchmark and modular architectures for continual learning, continual learning approaches for unsupervised learning, as well as seminal works like Elastic Weight Consolidation and Gradient Episodic Memory. 

About Us

At DeepMind, we've built a unique culture and work environment where long-term ambitious research can flourish. Our interdisciplinary team combines the best techniques from deep learning, reinforcement learning and systems neuroscience to build general-purpose learning algorithms. We have already made a number of breakthroughs towards building artificial general intelligence and we have all the ingredients in place to make further significant progress over the coming years!

The Role

Key responsibilities:

• Design, implement, and evaluate models, agents and training algorithms for large-scale continual learning.
• Report and present research findings and developments including status and results clearly and efficiently both internally and externally, verbally and in writing.
• Engage actively in team collaborations to meet research goals.
• Work with external collaborators and maintain relationships with relevant research labs and individuals.

About You

In order to set you up for success as a Research Scientist on this team, we look for the following skills and experience:

• PhD in a technical field or equivalent practical experience.

In addition, the following would be an advantage:

• PhD in machine learning, neuroscience or computer science.
• Relevant experience to the position such as post doctoral roles, a proven track record of publications, or deep neural network architectures.
• A real passion for AI!

Competitive salary applies.