Applied ML Graph Neural Network Research Software Engineer

As a software engineer at Google, you will work on projects critical to Google’s needs, with opportunities to switch teams and projects as the business grows and evolves. The mission is to research and develop production-ready, self-service ML tools, and support Google product teams in deploying ML solutions. In this role, you will join a fast-moving Applied ML group dedicated to bringing graph ML algorithms to solve industrial temporal and relational data problems at Google.

• Drive the end-to-end life cycle of new features, including brainstorming, literature review, development, and productionization.
• Help product teams (internal and external) deploy models.
• Identify, develop, and productionize GNN/ML methods driven by client feedback, team brainstorming, literature review, and original research applied into the GraphFlow GNN library.
• Develop and productionize industrial GNN/ML models and infrastructure for the GraphML for Google Systems cross-Product Area initiative to build a unified GNN and AI agent system to support Google's Systems.
• Partner closely with product teams and research groups to translate business requirements into ML solutions.

• Bachelor’s degree or equivalent practical experience.
• 5 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
• Experience designing, training, and deploying Graph Neural Networks (GNNs) or graph embedding models using frameworks such as PyTorch Geometric (PyG), JAX/Jraph, or TF-GNN.
• Ability to lead the Research and Development (R&D) and production of complex ML/Infra algorithms.

Google develops next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Products handle information at massive scale and extend well beyond web search.

You will be a key contributor in the GraphML for Google Systems cross-Product Area initiative, helping build a unified platform based on GNN and AI agents to monitor, optimize, and support core infrastructure across storage, networking, and compute.