Senior Research Scientist, Gemini Omni, DeepMind

Minimum qualifications:

• PhD in Computer Science, a related field, or equivalent practical experience.
• 3 years of experience in development with JAX, PyTorch, or TensorFlow.
• 3 years of experience with machine learning and machine learning algorithms.
• 3 years of experience with Generative Artificial Intelligence (GenAI) techniques (e.g., Large Language Models, Multi-Modal, Large Vision Models) or with GenAI-related concepts (language modeling, computer vision).
• 2 years of experience leading a research agenda.
• Experience in academic research within machine learning, publications, or research in related fields.

Preferred qualifications:

• 2 years of coding experience.
• 1 year of experience leading research efforts and influencing other researchers.

About the job:

We research and develop machine learning models for billions of Google users.

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 foundational research in next-generation generative modeling (e.g., autoregressive architectures, diffusion models) to pioneer breakthroughs across multimodal generation, including image, video, and audio synthesis.
• Formulate novel scientific methodologies and conduct deep literature reviews to solve complex, open-ended AI challenges, exercising independent judgment to balance immediate project milestones with long-term frontier research.
• Pioneer model optimization and efficient inference strategies to significantly reduce compute overhead, optimize latency, and scale large multimodal systems across high-performance infrastructure.
• Advance post-training and capability scaling using reinforcement learning to enhance model alignment, reasoning, and multi-turn generation quality across modalities.
• Apply rigorous engineering and experimental practices to design robust benchmarks, measure real-world performance, and systematically validate the scientific and practical impact of research findings.