About the job
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. EarthAI is a frontier initiative at Google Research to build the world’s most advanced planetary foundation models and bring it together with Gemini based reasoning to understand, plan, and act to help address some of the most critical challenges of our time. Our team develops models that interpret the world's physical dynamics—from global mobility and population shifts to environmental signals—transforming the entire planet into an interactive environment for understanding and reasoning We are looking for a Research Scientist to push the boundaries of multimodal generative AI and agentic systems at a massive scale.
Responsibilities
Design and implement algorithms for Generative AI across massive, multimodal planet-scale datasets, including satellite streams, population understanding and global environmental signals.
Lead the training and fine-tuning of Gemini models to achieve "planetary-aware" reasoning, enabling advanced task planning and multi-step execution in complex contexts.
Establish new benchmarks and publish original research that pushes this field forward.
Partner with cross-functional teams to deploy these models into products that promise to have a transformative impact.
Qualifications
Minimum
PhD in Computer Science, Artificial Intelligence, Machine Learning, Computer Vision, or equivalent practical experience.
Experience in AI research and development, including in Generative AI.
Experience in Python and machine learning frameworks (e.g., Jax, TensorFlow, PyTorch).
One or more accepted scientific publication submissions for conferences, journals, or public repositories (such as CVPR, ICCV, NeurIPS, ICML, ICLR, etc.)
Preferred
Experience in large-scale training of multimodal foundation models.
Experience with Large Language Models (LLMs) and their adaptation to complex reasoning and planning.
Ability to lead high-ambiguity research projects from high-level concepts to outcomes.
Ability to build and scale multi-agent systems.