(Senior) Researcher (f/m/d) - Foundation and World Models on Structured Business Data

What you'll do

At SAP’s Business AI Research, we explore the next frontier of AI methods on structured business data – graphs, tables, time series, and more. Foundation Models on Structured Data, such as SAP‑RPT‑1, usher in the GenAI era for business‑relevant scenarios that have traditionally been addressed with classical methods. We collaborate with existing applied research teams to create novel benchmarks for business problems, develop new architectures and models, and publish our work at top scientific conferences and in the open‑source community. We partner with academia to solve real business problems and advance the field.

• Pioneer technical advances in foundation models on structured data and explore world models for business applications.
• Gain insights by integrating rich semantic context with large‑scale business data.
• Enable academic research by providing relevant problem statements, frameworks, and data sets.
• Stay current with novel research, interpret findings, and benchmark against internal work.
• Translate applied research breakthroughs into publications at top‑tier conferences and contribute to thought leadership in foundation and world models on structured data.
• Collaborate closely with the team developing the relational pre‑trained transformer (SAP‑RPT‑1) to align internal initiatives with community best practices.
• Work with established academic partners and supervise PhD students during internships or collaborations.

What you bring

• PhD in Computer Science, Artificial Intelligence, Physics, Mathematics, or related discipline.
• Academic background in Foundation Models / Machine Learning on large‑scale structured data or World Models, with a substantial record of publications at top scientific conferences.
• Extensive research experience with machine learning on structured data and a deep understanding of how this translates to developing foundation models on structured data.
• Experience in Causal AI and its relation to foundation models on structured data is desirable.
• Proficiency in Python and hands‑on experience with ML frameworks such as PyTorch, TensorFlow, or similar. Optional experience with knowledge graph technologies.
• Professional experience with the combination of knowledge graphs and large language models in the ERP domain is a plus.

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Requisition ID: | Work Area: Software‑Research | Expected Travel: 0 – 10% | Career Status: Professional | Employment Type: Regular Full Time