Research Scientist, Agent Post Training, DeepMind

As an organization, Google maintains a portfolio of research projects driven by fundamental research, new product innovation, product contribution and infrastructure goals, while providing individuals and teams the freedom to emphasize specific types of work. As a Research Scientist, you'll setup large-scale tests and deploy promising ideas quickly and broadly, managing deadlines and deliverables while applying the latest theories to develop new and improved products, processes, or technologies. From creating experiments and prototyping implementations to designing new architectures, our research scientists work on real-world problems that span the breadth of computer science, such as machine (and deep) learning, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more.

As a Research Scientist, you'll also actively contribute to the wider research community by sharing and publishing your findings, with ideas inspired by internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.

Artificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.

Responsibilities

• Drive the research process for large-scale agent post-training from hypothesis formulation to delivery in the Gemini model recipe.
• Design and execute ablation studies to validate research hypotheses and accelerate experimental feedback loops.
• Communicate research findings, progress, and outcomes to the broader team through visualizations and reports.
• Develop research infrastructure and utilities for data analysis and model evaluations using standard engineering practices.
• Collaborate with other research scientists and engineers to maintain a regular feedback and communication loop.

Minimum qualifications:

• PhD in Computer Science, Machine Learning, or a related quantitative field, or equivalent practical experience.
• 2 years of experience with machine learning frameworks such as JAX, Flax, or PyTorch
• Experience conducting research in reinforcement learning, tool use, or agentic systems.

Preferred qualifications:

• Experience publishing research in reinforcement learning, reinforcement learning from human feedback, or tool-use algorithms at machine learning venues.
• Experience working with large-scale distributed training infrastructure and scaling experiments.
• Research experience in systems design, code complexity, or working with large-codebase environments.
• Experience developing simple, scalable solutions for complex, open-ended research problems.