Research Scientist, Reinforcement Learning, DeepMind

Requirements:

• PhD in Machine Learning, or equivalent practical experience.
• 2 years of experience implementing algorithms within research codebases.
• Experience conducting research in reinforcement learning, including contributions to peer-reviewed publications.
• Experience designing and executing end-to-end experiments, including setup, analysis, and interpretation.

Nice to haves:

• Experience with advanced reinforcement learning topics, such as RL for sequence models, post-training, preference-based learning, or agentic systems.
• Familiarity with modern research stacks (e.g., JAX/Flax or PyTorch) and experience scaling experiments.
• Strong experimental judgment, including selecting appropriate baselines and designing insightful ablations.
• Comfort with scaling methodologies, evaluation techniques, and diagnosing complex failure modes.
• High agency and drive to push projects forward, prioritize effectively, and take initiative.
• Excellent communication skills, with a focus on clear and honest presentation of research results.

What you'll be doing:

• Propose and pursue novel research directions by formulating and testing hypotheses.
• Implement algorithmic ideas and conduct end-to-end experiments, analyzing results and iterating on findings.
• Design evaluations and ablations to answer key questions and drive research decisions.
• Build and improve infrastructure to enable research at scale.
• Communicate research findings through write-ups, presentations, and publications, while fostering a culture of high standards and constructive feedback.

Perks and benefits:

• Opportunity to work at Google DeepMind, a pioneering AI lab focused on advancing AI development.
• Contribute to real-world problems spanning a broad range of computer science fields.
• Collaborate with a tight-knit team of scientists and engineers on large-scale reinforcement learning projects.
• Chance to publish findings and collaborate with research programs globally.