As a part of the Graph Neural Network (GNN) team, you will bridge relational structure and deep learning, making it seamless to integrate graph-structured data into neural networks and foundation models at massive scale. As a part of the team, you will conduct fundamental research and build production-grade frameworks to train and deploy these models over complex, heterogeneous graphs. Recently, the team's work has expanded into generative AI, developing structured data encoders, graph tokens, hybrid graph-sequence architectures, and graph-based retrieval and agentic systems to enhance Large Language Models (LLMs).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.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Author research papers to share and generate impact of research results across function and in the research community.
- Help in growing research business by sharing research trends and best practices within the community.
- Drive project work by defining the data structure, framework, design, and evaluation metrics for research solution development and implementation. Identify timelines and obtain resources needed.
- Discover new and upcoming research areas by interacting with potential external and internal collaborators, helping to develop long-term research strategies and plans to expand the impact of Google research.
- Identify defined problems/gaps in existing technology and engage stakeholders and leaders to address them.
Minimum qualifications:
- PhD in Computer Science, a related field, or equivalent practical experience.
- 2 years of experience leading a research agenda.
- Experience with AI/ML technologies (e.g., frameworks for deep learning (PyTorch, TensorFlow, Jax, Ray), AI accelerators (TPUs, GPUs), model architectures (encoders, decoders, transformers), or using machine learning APIs).
- One or more scientific publication submission(s) for conferences, journals, or public repositories (such as CVPR, ICCV, NeurIPS, ICML, ICLR, etc.).
Preferred qualifications:
- 1 year of experience leading research efforts and influencing other researchers.