Senior Research Scientist, Graph Neural Network

Job description

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. About the job As a Research Scientist on the Graph Neural Network (GNN) team within OMEGA, you will pioneer research at the intersection of deep learning, structured data, and Large Language Models (LLMs). In this role, you will invent novel neural architectures, graph encoders, and agentic retrieval methods that bring relational reasoning to foundation models at massive scale.

You will collaborate closely with partners across Google Research, Google DeepMind, and core product teams to translate theoretical breakthroughs into production-grade frameworks and next-generation AI capabilities. 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).