Applied ML Graph Neural Network Research Software Engineer

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.

Our mission is to research and develop production-ready, self-service ML tools, and to support Google product teams on 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 in Google. You will drive end-to-end life cycle of new features (brainstorming, literature review, development, productionization) and helping product teams (internal and external) deploy models.

You will also 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.

Minimum qualifications:

- 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.

Preferred qualifications:

- Ability to lead the Research and Development (R&D) and production of complex ML/Infra algorithms.

- 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.