About the job
At Google DeepMind, we prioritize diversity in experience, knowledge, backgrounds, and perspectives, leveraging these attributes to create remarkable impact. We are dedicated to providing equal employment opportunities regardless of sex, race, religion, belief, ethnic or national origin, disability, age, citizenship, marital status, domestic or civil partnership status, sexual orientation, gender identity, pregnancy, or any other condition protected by applicable law. If you have a disability or additional needs that require accommodation, please feel free to reach out to us.
Snapshot
We seek talented Research Scientists to advance fundamental research and technology in Artificial Intelligence, as part of our interdisciplinary and collaborative Reinforcement Learning team.
About Us
DeepMind’s RL team is a cohesive and collaborative group of scientists and engineers, led by Tom Schaul. We address large-scale research challenges in reinforcement learning, designing, refining, and scaling RL algorithms to yield significant scientific and product impact. Over the past decade, our RL team has been pivotal in developing innovations such as DQN, AlphaGo, Rainbow, AlphaZero, MuZero, AlphaStar, AlphaProof, and Gemini. Join us in creating the next groundbreaking advancement!
The Role
As a Research Scientist, you will leverage your machine learning expertise and technical skills to innovate, spearhead research projects, and apply findings to impactful challenges. You will be responsible for implementing code, conducting experiments, owning results from start to finish, communicating findings effectively, and collaborating with fellow team members to empower others.
Your work may involve:
- Initiating and pursuing novel research directions by proposing and testing hypotheses.
- Implementing algorithmic ideas and executing comprehensive experiments, including setup, execution, analysis, and iteration.
- Sharing your expertise and insights with other researchers.
- Building or enhancing infrastructure to support scalable research.
- Designing evaluations and ablations that address real questions and influence perspectives.
- Meticulously analyzing results, including debugging and failure analysis.
- Effectively communicating through visualizations, write-ups, and publication-ready narratives and figures.
- Contributing to a culture of first-principles thinking, high standards, and constructive feedback.
Our projects encompass a broad spectrum of state-of-the-art machine learning and AI domains, including large language models, distributed machine learning, and more.
