Vision Research Scientist

• Design, implement, and scale state-of-the-art LLM's core image/video understanding capabilities.
• Drive end-to-end experimental workflows across pre-training and post-training.
• Build robust, scalable infra and data pipelines to support high-quality multimodal training and eval benchmarks.
• Partner closely with Research Scientists and SWEs to transition cutting-edge research prototypes into production-ready foundation models, striking the right balance between rapid prototyping, maintainability, and generality.
• Analyze complex experiment metrics and failure modes across multimodal benchmarks to identify opportunities for model quality hillclimbing.

Minimum qualifications:

• 3 years of experience in software engineering or related technical field.
• 2 years of work experience in an applied research setting.
• Experience in Artificial Intelligence or Machine Learning.
• Experience developing autonomous agents, multi-agent workflows, or agentic frameworks (e.g., AutoGen, CrewAI, LangGraph, tool-use/function-calling, or multi-agent RL); and experience designing, pre-training, fine-tuning, or evaluating Large Language Models (LLMs) and multimodal foundation models.
• Contribution to research communities or efforts, including publishing papers (being listed as author) at conferences such as NIPS, ICML, ACL, CVPR, etc.

Preferred qualifications:

• Master's degree or PhD degree in Computer Science, Artificial Intelligence, Machine Learning, or related technical field.
• Demonstrated ability to independently manage data generation, model training, and optimization pipelines.

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 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.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Google.Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Mountain View, CA, USA; New York, NY, USA; Los Angeles, CA, USA.

• 3 years of experience in software engineering or related technical field.
• 2 years of work experience in an applied research setting.
• Experience in Artificial Intelligence or Machine Learning.
• Experience developing autonomous agents, multi-agent workflows, or agentic frameworks (e.g., AutoGen, CrewAI, LangGraph, tool-use/function-calling, or multi-agent RL); and experience designing, pre-training, fine-tuning, or evaluating Large Language Models (LLMs) and multimodal foundation models.
• Contribution to research communities or efforts, including publishing papers (being listed as author) at conferences such as NIPS, ICML, ACL, CVPR, etc.