Research Scientist, Gemini Omni, 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.

As a part of the Research Scientist, your role involves applying scientific expertise to advance GDM's mission. You will work independently on research assignments, driving project segments, which involves conducting literature reviews, using scientific methods and independent judgment to develop new methods solving challenging problems, balancing short-term and long-term goals, taking engineering approaches to measure, validate and evaluate the impact of research.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 various learning opportunities and career pathways for those driven to achieve exceptional results through collective effort.

Responsibilities:

• Optimize model architectures and training pipelines for distributed training, maximizing compute efficiency across accelerator clusters (TPUs/GPUs).
• Design and train state-of-the-art generative models across multi-modal domains (image, video, and audio), leveraging modern architectures such as diffusion models, flow matching, and autoregressive frameworks.
• Develop and deploy reinforcement learning algorithms (e.g., RLHF, DPO, policy gradient methods) to align model behaviors, enhance generation quality, and improve multi-step reasoning capabilities.
• Drive efficient inference strategies through techniques such as model quantization, pruning, distillation, speculative decoding, and kernel optimization to minimize latency and memory footprint in production.
• Bridge research and production engineering by translating novel generative AI breakthroughs into scalable, robust algorithms and evaluation benchmarks.

Minimum qualifications:

• PhD degree in computer science, mathematics, applied statistics, machine learning or equivalent practical experience.
• Experience with training generative models (Large Language Model (LLM), image, video).
• Experience of TensorFlow or ML frameworks (e.g. JAX or PyTorch).
• Experience conducting research.
• Publication record in AI conferences (e.g., NeurIPS, CVPR, ICCV, ICLR).

Preferred qualifications:

• Experience with large-scale data pipelines.
• Experience with training large-scale models.
• Proven experience conducting research in industry