Google DeepMind · London
Research Scientist, Planning, Reasoning, Inference & Structured models (PRISM)
10/14/2025
Description
As a research scientist, you’ll be responsible for the following:
Key responsibilities:
- Define unsolved problems in the development of AI systems, propose solutions to tackle these issues, ranging from model architecture, data generation process, training algorithm or paradigm, and inference-time techniques.
- Carry out a rigorous process of implementation of these ideas in large-scale codebases on massive-compute infrastructure.
- Conduct thorough scientific analysis to ascertain the value of the proposed methods.
- Collaborate with partners and carry the most promising methods into production.
- Drive innovation from concept to implementation: Stay at the forefront of the AI field, identifying and exploring novel research avenues, implementing cutting-edge techniques, and validating their effectiveness through comprehensive experimental analysis.
- Provide technical guidance and mentorship to other engineers and researchers.
- Take ownership of critical issues and drive collaborative efforts to ensure project success.
Qualifications
- Possesses a deep intellectual curiosity and a relentless drive to understand the intricacies of machine learning models and training systems.
- A true team player who prioritizes collective success. This person is flexible, collaborative, and willing to contribute wherever needed to achieve project goals, fostering a positive and supportive team environment.
- Embraces a hacker mindset, fearlessly diving into complex codebases and technical challenges. Capable of both rapid prototyping for experimentation and developing robust, production-ready code for long-term solutions.
- Programming experience in Python and machine learning libraries such as jax, tensorflow or pytorch.
- Experience implementing state of the art machine learning algorithms and understand how to analyze results using notebooks and libraries such as pandas.
- Great communication skills, able to explain ideas at the conceptual level.
- A PhD degree in computer science, math or physics.
- Experience with serving and training Large Language Models.
- Knowledge of hardware acceleration of machine learning algorithms.
- Knowledge of distributed computing.
- Interest in reinforcement learning.