Research Scientist, Strategic Bets, 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 Deep Mind, 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.

We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.

• PhD degree in Machine learning, AI, or a related computational field, or equivalent practical experience.
• Experience in delivering research impact through publications, open-source contributions, or deployed systems.
• Experience with large language models, agentic workflows (e.g., tool use, decomposition, multi-agent coordination), or inference-time reasoning architectures.
• Programming experience across common scripting languages and ML pipelining tools.
• Experience in probabilistic modeling, uncertainty quantification, calibration, or reinforcement learning.

• Experience with prediction markets, quantitative research, temporal data modeling, or real-world forecasting benchmarks.
• Familiarity with process-based reward modeling, automated design of agentic systems, or search/retrieval-augmented generation.

• Engage with existing research efforts to build AI systems capable of superhuman probabilistic estimation, focusing on domains with no clean time series or stable reference classes.
• Collaborate with researchers and engineers to design novel structured reasoning approaches, iterative self-improvement loops, and multi-agent workflows that improve estimation quality and calibration.
• Research solutions to core forecasting issues, including temporal reasoning, information retrieval and filtering, coherent world-modeling, and novel reward structures for multi-step reasoning.
• Engage with product teams and enterprise partners to drive the deployment of our research into high-stakes decision-making environments.
• Report and present research findings clearly and efficiently both internally and externally, and suggest team collaborations to meet research goals for the wider Science program.