Senior Research Scientist, Gemini Omni, DeepMind

Socket.dev; Mountain View (CA); On-site; USD 174,000 - 252,000; Full time

Benefits offered by this job

Bonus target

Equity

Benefits

Job summary

Google DeepMind is seeking a senior researcher to drive foundational work in next‑generation generative modeling across multimodal domains, including image, video, and audio synthesis. You will formulate novel methodologies and conduct deep literature reviews to tackle open challenges.

With 3+ years in ML/GenAI and a proven record of leading research, you will advance optimization, efficient inference, and RL-based scaling while collaborating with interdisciplinary teams to impact products and

Qualifications

• PhD in Computer Science, a related field, or equivalent practical experience.
• 3 years of development with JAX, PyTorch, or TensorFlow.
• 3 years of experience with machine learning and ML algorithms.
• 3 years of GenAI techniques or GenAI concepts (language modeling, CV, multimodal).
• 2 years leading a research agenda.
• Experience in academic research, publications, or related fields.

Responsibilities

• Drive foundational research in next-generation generative modeling across multimodal generation (image, video, audio).
• Formulate novel scientific methodologies and conduct literature reviews to solve open-ended AI challenges.
• Pioneer model optimization and efficient inference to reduce compute overhead and scale systems.
• Advance post-training and capability scaling using reinforcement learning to improve model alignment and multi-turn generation.
• Apply rigorous engineering practices to design benchmarks and validate impact of research findings.

Skills

GenAI research

ML algorithms

Leadership of research

Publications/academic research

Independent judgment

Education

PhD or equivalent practical experience

Tools

JAX

PyTorch

TensorFlow

Job description

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.

We are pushing the boundaries across multiple domains. Our global teams offer 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: $174000 - $252000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

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.