Research Scientist, GenAI, 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. 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. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits Learn more about benefits at Google . • Define and lead strategic, high-impact research programs advancing state-of-the-art artificial intelligence models, algorithms, and theory. • Provide technical leadership across multi-disciplinary research pods, guiding technical directions, experimental design, and execution. • Publish groundbreaking research findings in peer-reviewed artificial intelligence and machine learning conferences and scientific journals. • Collaborate closely with software engineering, infrastructure, and product teams to translate foundational research advances into production-grade systems and platform capabilities. Mentor, coach, and guide early-to-mid-career research scientists and engineers, fostering a culture of scientific excellence and innovation. • Advocate responsible AI development, ensuring safety, alignment, ethics, and are integrated into all research initiatives from inception. Minimum qualifications: • PhD degree in Computer Science, Artificial Intelligence, Machine Learning, Applied Mathematics, Computational Neuroscience, or a related quantitative scientific discipline, or equivalent practical research experience. • Experience in leading research projects from ideation to completion, including managing technical roadmaps and experimental workflows. • Experience in Python and deep learning frameworks (e.g., JAX, PyTorch, TensorFlow). • Experience with peer-reviewed publications as lead or primary author in AI/ML venues (e.g., NeurIPS, ICML, ICLR, CVPR, ACL, Nature, Science). Preferred qualifications: • Experience as a subject-matter expert in a core subfield of AI, such as large foundation models, reinforcement learning, reasoning, agentic systems, or scientific AI applications. • Experience bridging the gap between theoretical research breakthrough and large-scale practical product or platform integration. • Ability to communicate complex scientific concepts effectively to executive leadership, technical partners, and non-specialist audiences. • Track record of collaborative research environments and driving cross-organization initiatives.