Google's software engineers develop next-generation technologies that change how billions of users connect, explore, and interact with information. This role focuses on developing state-of-the-art methods for AI generative models to solve complex geospatial problems, architecting, designing, and overseeing the experimentation and development of complex, large-scale systems that fuse geospatial data modalities.
Requirements
- Experience in Python and machine learning frameworks (e.g., Jax, TensorFlow, PyTorch).
- Deep understanding and experience with Large Language Models (LLMs) and their application to agentic systems.
- Deep technical knowledge of multi-modal machine learning, geospatial reasoning, knowledge graphs.
- Experience with software design and architecture.
- Innovating new data representations, designing novel model architectures for spatial reasoning, and optimizing performance on cutting-edge hardware (TPUs, GPUs).
Responsibilities
- Set the research and technical vision and strategic direction for the team's research and development efforts towards state-of-the-art models in Generative AI for complex global geospatial problems.
- Own the technical architecture for large-scale geospatial reasoning systems.
- Design resilient, scalable, and efficient systems that can handle petabytes of data and serve sophisticated reasoning models.
- Work on the most ambiguous and challenging technical problems in the field, this includes innovating new data representations, designing novel model architectures for spatial reasoning, and optimizing performance on cutting-edge hardware (TPUs, GPUs).
- Architect, design, and oversee the experimentation and development of complex, large-scale systems that fuse geospatial data modalities (e.g., satellite imagery, street-level data, population data, knowledge graphs) to enable advanced geospatial reasoning.
- Act as the primary technical point of contact for external teams, collaborating with other Google organizations to integrate and deploy your team's research.
Other
- Qualified applications with arrest or conviction records will be considered for employment in accordance with the San Francisco Fair Chance Ordinance for Employers and the California Fair Chance Act.
- Preferred qualifications: Master's degree or PhD in Computer Science, Geoinformation Science, or a related field, with a focus on machine learning, computer vision.
- Proven track record of leading a team of engineers, with experience mentoring and growing talent.
- A history of designing and shipping complex, large-scale technical projects from end-to-end.
- One or more accepted scientific publication submissions for conferences, journals, or public repositories (such as CVPR, ICCV, NeurIPS, ICML, ICLR, etc.).
