Limited ; f​/m​/d - Neuro-Symbolic AI: Frontier

Research Fellow In The Global Content Group (Gcg) Engineering Team

At SAP, we keep it simple: you bring your best to us, and we'll bring out the best in you. We're builders touching over 20 industries and 80% of global commerce, and we need your unique talents to help shape what's next. The work is challenging – but it matters. You'll find a place where you can be yourself, prioritize your wellbeing, and truly belong.

What's in it for you? Constant learning, skill growth, great benefits, and a team that wants you to grow and succeed.

As a Research Fellow in the Global Content Group (GCG) Engineering team, you will:

• Research and prototype neuro-symbolic AI approaches that combine the reasoning power of structured knowledge graphs with the generalization capabilities of large language and foundation models
• Develop methods to inject ontology-level knowledge — concepts, relationships, and constraints — directly into foundation models, enabling them to generate accurate, schema-aware SPARQL queries from natural language
• Design and evaluate knowledge-grounded question answering (Q&A) systems that go beyond retrieval-augmented generation, leveraging formal ontologies to improve precision, explainability, and faithfulness of model outputs
• Benchmark your approaches against existing text-to-SPARQL and semantic Q&A baselines on enterprise knowledge graphs
• Collaborate with a global team of software engineers, data scientists, and researchers on cutting-edge challenges at the intersection of symbolic AI and modern deep learning
• Document and present your research findings, contributing to publications and internal knowledge-sharing

What you bring:

• PhD candidate (mid-to-late stage) or postdoctoral researcher in Computer Science, Artificial Intelligence, Natural Language Processing, or a related field
• Strong background in neuro-symbolic AI, knowledge representation, or semantic web technologies (RDF, OWL, SPARQL)
• Hands-on experience with large language models or foundation models — fine-tuning, prompt engineering, or knowledge injection techniques
• Familiarity with text-to-SPARQL, semantic parsing, or knowledge graph question answering (KGQA)
• Solid programming skills in Python; experience with ML frameworks (PyTorch, Hugging Face) and knowledge graph tooling is a plus
• Research track record in neuro-symbolic systems, NLP, or knowledge graphs — with publications
• Ability to drive independent research and translate findings into working prototypes
• Good communication skills in English (written and spoken);
  German is a plus

Where you belong:

We sit at the frontier of enterprise AI — combining structured ontological knowledge with the expressive power of foundation models to build Q&A systems that are both intelligent and trustworthy. Join a passionate team of engineers and researchers pushing the boundaries of neuro-symbolic AI at scale.