Senior AI Researcher (Relational Foundation Models)

Location: Palo Alto, United States; hybrid work pattern

Salary: USD 148,600–306,300 per year

Company

SAP develops enterprise applications and business AI that support business-critical operations across finance, procurement, HR, supply chain, and customer experience.

What you will do

• Lead original research into relational foundation models for multi-table enterprise databases.
• Design model architectures, pretraining strategies, learning objectives, and inference methods for relational and structured data.
• Run distributed GPU experiments, ablation studies, evaluation benchmarks, and rigorous baseline comparisons.
• Develop datasets, synthetic data generators, and evaluation frameworks for relational learning.
• Translate research findings into scalable prototypes for engineering and product integration.
• Mentor researchers and engineers, provide technical leadership, and publish at leading AI research venues.

Requirements

• PhD in Machine Learning, Computer Science, Statistics, Applied Mathematics, or a closely related field, or equivalent demonstrated research experience.
• Strong publication record at leading venues such as NeurIPS, ICML, ICLR, KDD, AAAI, or AISTATS.
• Deep expertise in foundation-model techniques, including Transformer architectures, attention mechanisms, large-scale pretraining, in-context learning, and transfer learning.
• Hands-on experience with tabular machine learning, graph neural networks, relational learning, or structured prediction.
• Strong Python and PyTorch skills, including independently implementing, debugging, and evaluating research ideas end to end.
• Ability to conduct rigorous experiments and work independently from problem identification through validated results.

Nice to have

• Experience with relational deep learning, heterogeneous graph learning, synthetic data generation, or large-scale pretraining.
• Knowledge of relational databases, SQL, or data-warehouse architectures.
• Experience with distributed training, benchmark development, or open-source machine learning libraries.

Culture & Benefits

• Flexible working models and an inclusive culture focused on health and well-being.
• Opportunity to collaborate with teams around the world and participate in global forums.
• Autonomy to explore new research methods and support for scaling research impact.
• Variable incentive opportunities and additional benefits may be available according to applicable plans and policies.