Applied ML Graph Neural Network Research Software Engineer

• 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.

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.

• 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.