Research Scientist, Gemini Omni, DeepMind

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward. The Core team builds the technical foundation behind Google’s flagship products. We are owners and advocates for the underlying design elements, developer platforms, product components, and infrastructure at Google. These are the essential building blocks for excellent, safe, and coherent experiences for our users and drive the pace of innovation for every developer. We look across Google’s products to build central solutions, break down technical barriers and strengthen existing systems. As the Core team, we have a mandate and a unique opportunity to impact important technical decisions across the company. Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $147000 - $210000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google. 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.

Qualifications 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