Generative AI Research Scientist

Google DeepMind; Mountain View (CA); On-site; USD 147,000 - 210,000; Full time

Job summary

Google DeepMind in Mountain View, CA, seeks a Research Scientist to advance Gemini through large-scale RL environments, rigorous evaluations, and scalable deployments. You will shape experiments, design architectures, and contribute to core AI research that translates into real-world products and safety-conscious innovations.

The role emphasizes collaboration with interdisciplinary teams, tackling challenging problems across security, hardware, and performance engineering, while sharing findings

Qualifications

• PhD degree in Computer Science, Artificial Intelligence, Machine Learning, a related technical field, or equivalent practical experience.
• 1 year experience with Generative AI, Large Language Models, natural language processing, or Agent-based systems.
• 1 year of experience working on modern large language model post-training (e.g., SFT, RLHF, DPO, PPO), model alignment, or core generative model development in an industry AI lab, research institute, or frontier AI organization.

Responsibilities

• Improve Gemini with new Reinforcement Learning (RL) environments, evaluations, changes to the RL recipe, or ideas.
• Explore domains where Gemini should be superhuman and build the evaluations that show the gap is real.
• Invent new ways of manufacturing hard problems and the graders that make them verifiable.
• Evaluate and train Gemini on your environments, read the trajectories to see what it is actually learning, and land what transfers into production.
• Contribute to identifying and delivering breakthroughs for Gemini.

Skills

Generative AI experience

LLM post-training experience

Agent-based systems

Education

PhD in Computer Science / AI / ML or equivalent practical experience

Job description

Google DeepMind in Mountain View, CA, seeks a Research Scientist to advance Gemini through large-scale RL environments, rigorous evaluations, and scalable deployments. You will shape experiments, design architectures, and contribute to core AI research that translates into real-world products and safety-conscious innovations.

The role emphasizes collaboration with interdisciplinary teams, tackling challenging problems across security, hardware, and performance engineering, while sharing findings