About the job
Research Scientist in AI-Driven Scientific Discovery
Location: Montreal, Canada
At Google DeepMind, we prioritize diverse experiences, knowledge, backgrounds, and perspectives, leveraging these attributes to create remarkable impacts in the field of artificial intelligence. We are dedicated to providing equal employment opportunities for all individuals, regardless of sex, race, religion, ethnic origin, disability, age, citizenship, marital status, sexual orientation, gender identity, or any other basis protected by applicable law. If you require accommodations due to a disability or other needs, please let us know.
Position Overview
We are seeking a Research Scientist to become an integral part of our Montreal team, focused on utilizing AI for Scientific Discovery. The team’s research encompasses systems that integrate code execution and retrieval tools with natural-language scientific knowledge, aimed at accelerating groundbreaking scientific discoveries. Research topics include general-purpose algorithms that utilize Large Language Models (LLMs) for efficient search and exploration, fine-tuning LLMs with Reinforcement Learning, and engaging in open-ended tasks that contribute to large-scale AI-powered empirical research.
About Google DeepMind
Artificial Intelligence stands as one of humanity’s most transformative inventions. At Google DeepMind, our team of scientists, engineers, and machine learning experts collaborates to push the boundaries of AI technology for the betterment of society and scientific advancement, while ensuring that safety and ethics are prioritized in all our endeavors.
Role Responsibilities
As a Research Scientist at Google DeepMind, you will lead initiatives to develop innovative algorithmic architectures with the ultimate goal of realizing Artificial General Intelligence.
Your key responsibilities will include:
- Designing, implementing, and evaluating models, agents, and software prototypes of large foundational models.
- Advancing the state of the art in reinforcement learning methods and machine learning optimization techniques to construct autonomous scientific discovery systems.
- Communicating research findings and advancements clearly and effectively, both internally and externally, through written and verbal channels.
- Collaborating with team members to achieve ambitious research objectives.
- Engaging with external partners and maintaining collaborations with relevant research laboratories.
