Applied ML Graph Neural Network Research Software Engineer

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

Applied ML Graph Neural Network Research Software Engineer

Share Applied ML Graph Neural Network Research Software Engineer

X In most instances, this position requires in-person interviews as part of the hiring process.

• 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.

About the job

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.

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.

Google is proud to be an equal opportunity and affirmative action employer. We are committed to building a workforce that is representative of the users we serve, creating a culture of belonging, and providing an equal employment opportunity regardless of race, creed, color, religion, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition (including breastfeeding), expecting or parents-to-be, criminal histories consistent with legal requirements, or any other basis protected by law. See also Google's EEO Policy , Know your rights: workplace discrimination is illegal , Belonging at Google , and How we hire .

Google is a global company and, in order to facilitate efficient collaboration and communication globally, English proficiency is a requirement for all roles unless stated otherwise in the job posting.

Equity is granted exclusively and discretionarily by Alphabet Inc. on the basis of an agreement concluded between you and Alphabet Inc. Alphabet Inc. is your sole contractual partner with respect to equity grants. GSU grants are not guaranteed, are discretionary, are subject to approval by the Alphabet Inc. board of directors or its delegate, the terms of the relevant Alphabet Inc. stock plan, and your grant agreement. They have no impact on statutory payments. Current or past grants do not confer an acquired right.