Applied ML & GNN Engineer - Research to Production

Google LLC; Zürich; On-site; CHF 140,000 - 210,000; Full time

Job summary

Google LLC in Zürich is seeking an Applied ML Graph Neural Network Research Software Engineer to advance end-to-end ML workflows for graph data. You will design, train and deploy GNN models, collaborate with product teams, and productionize ML infrastructure at scale across Google's systems.

The role emphasizes building scalable graph ML solutions, leading R&D efforts, and contributing to GraphML initiatives across Google Systems. This position requires in-person interviews in most cases.

Qualifications

• Bachelor’s degree or equivalent practical experience.
• 5 years of experience with ML infrastructure (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.

Responsibilities

• 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.

Skills

Graph Neural Networks

ML infrastructure

GNN development

Education

Bachelor’s degree or equivalent practical experience

Tools

PyTorch Geometric (PyG)

JAX/Jraph

TF-GNN

Job description

Google LLC in Zürich is seeking an Applied ML Graph Neural Network Research Software Engineer to advance end-to-end ML workflows for graph data. You will design, train and deploy GNN models, collaborate with product teams, and productionize ML infrastructure at scale across Google's systems.

The role emphasizes building scalable graph ML solutions, leading R&D efforts, and contributing to GraphML initiatives across Google Systems. This position requires in-person interviews in most cases.