About the job

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.

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.

As a Research Scientist, you will push the boundaries of multimodal generative AI and agentic systems at a massive scale.

Google Research is building the next generation of intelligent systems for all Google products. To achieve this, we’re working on projects that utilize the latest computer science techniques developed by skilled software developers and research scientists. Google Research teams collaborate closely with other teams across Google, maintaining the flexibility and versatility required to adapt new projects and foci that meet the demands of the world's fast-paced business needs.

The US base salary range for this full-time position is $166,000-$244,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

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.

Minimum qualifications

• PhD in Computer Science, Artificial Intelligence, Machine Learning, Computer Vision, or equivalent practical experience.

• 2 years of experience in AI research and development, including in generative AI.

• Experience in Python and machine learning frameworks (e.g., Jax, Tensor Flow, Py Torch).

• One or more accepted scientific publication submissions for conferences, journals, or public repositories (such as CVPR, ICCV, NeurIPS, ICML, ICLR, etc.).

Preferred qualifications

• Experience with Large Language Models (LLMs) and their adaptation to complex reasoning and planning.

• Experience in large-scale training of multimodal foundation models.

• Ability to lead high-ambiguity research projects from high-level concepts to exceptional outcomes.

• Ability to successfully build and scale multi-agent systems.