Frontier AI Research Scientist

• Drive foundational post-training and advanced reasoning research across both large language models (LLMs) and diffusion architectures.
• Develop next-generation post-training techniques—specifically focusing on advancing self-distillation methods, scaling RLxF for complex agentic capabilities, and developing novel environment-in-the-loop RLxF recipes.
• Operate within our highly collaborative environment; bridge the gap between algorithmic innovation and deployment by partnering closely with GDM verticals to integrate your breakthroughs directly into products/services, including Thinking Strike, Veo/Omni, and Science.
• Take end-to-end ownership of your algorithms, ensuring your work continuously feeds GDM's production systems.
• Contribute to compiling internal research manuscripts and publishing research in venues (e.g., ICLR, ICML, NeurIPS, but subjected to GDM approvals).

Minimum qualifications:

• Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, a related technical field, or equivalent practical experience.
  
• 2 years of experience with ML/AI algorithms, tools, deep learning, or other AI/ML modeling.
• 2 years of experience in Python and a related machine learning package (e.g., Keras, PyTorch and Hugging Face Transformers).
• 1 year of experience developing algorithms for intelligent agents, including areas such as planning, reinforcement learning, or multi-agent systems.
• Experience in artificial intelligence or machine learning.

Preferred qualifications:

• Master's degree or PhD in Computer Science, Artificial Intelligence, Machine Learning, or related technical field.
• Deep expertise in Large Language Model (LLM) post-training techniques, particularly Reinforcement Learning and Reward Modeling at scale.
• Track record of publications at NeurIPS, ICLR, ICML.

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.

Artificial intelligence will be one of humanity’s most transformative inventions. At 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.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.

• Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, a related technical field, or equivalent practical experience.
  
• 2 years of experience with ML/AI algorithms, tools, deep learning, or other AI/ML modeling.
• 2 years of experience in Python and a related machine learning package (e.g., Keras, PyTorch and Hugging Face Transformers).
• 1 year of experience developing algorithms for intelligent agents, including areas such as planning, reinforcement learning, or multi-agent systems.
• Experience in artificial intelligence or machine learning.