Senior AI Researcher - Relational Foundation Models

Senior AI Researcher – Relational Foundation Models Full-time Compensation: USD 148,600 - USD 306,300 - yearly You’ll collaborate with teams around the world, mentor rising talent, and participate in global forums. SAP’s growth culture gives you the autonomy to explore new methods and the support to scale your impact internationally. You’ll architect end-to-end solutions that unlock new product capabilities and operational efficiencies. You’ll drive roadmap decisions with evidence, lead complex analyses, and ensure models are interpretable, reliable, and aligned to business outcomes. Your insights will influence strategy across functions. Summary SAP is uniquely positioned to lead the next wave of AI by infusing intelligence directly into the business processes that run the world. Our team's mission is to develop Foundation Models for structured data. We will start by launching and continuously improving SAP-RPT-1, the first version of our relational model portfolio. This is part of our commitment to lead research in a field where SAP's expertise in enterprise data provides us with a solid competitive advantage. As a Senior AI Researcher, you will improve Relational Foundation Models (RFMs). These models are general‑purpose. They learn, reason, and make predictions from complex, multi‑table enterprise data. They operate without the need for manual feature engineering or task‑specific pipelines. Your work will span fundamental research and scalable system development, from initial hypothesis through production‑ready methods that operate over real enterprise databases. We are looking for innovators ready to bridge the gap between cutting‑edge AI research and the complex, structured reality of global enterprise data. What You’ll Do Lead original research into Relational Foundation Models. These models are architectures that learn from many multi‑table enterprise databases. They can also adjust to different schemas and prediction tasks. Design and develop novel model architectures, pretraining strategies, learning objectives, and inference methods for relational and structured data. Run large‑scale distributed GPU experiments; build rigorous baselines, ablation studies, and evaluation benchmarks. Contribute to datasets, synthetic data generators, and evaluation frameworks for relational learning. Translate research findings into scalable prototypes ready for integration with engineering and product teams. Mentor researchers and engineers and provide technical leadership on research projects. Publish at top‑tier venues (NeurIPS, ICML, ICLR, KDD, or equivalent) and contribute to the broader AI research community. Stay current with the latest advances in foundation models, tabular learning, graph learning, and large‑scale machine learning. What You Bring Required 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. You should have deep expertise in modern foundation‑model techniques. This includes Transformer architectures and attention mechanisms. It also includes large‑scale pretraining, in‑context learning, and transfer learning. Hands‑on experience in at least one of: tabular machine learning, graph neural networks, relational learning, or structured prediction. Excellent Python and PyTorch skills with the ability to independently implement, debug, and evaluate research ideas end‑to‑end. Rigorous experimental practice: strong baselines, controlled ablations, honest failure analysis, and awareness of data leakage and evaluation artifacts. Ability to operate with substantial research independence — from identifying problems through validated results. Preferred Experience with relational deep learning, graph neural networks, or heterogeneous graph learning. In‑context learning is not limited to natural‑language tasks. It also covers meta‑learning and amortized Bayesian inference. Create synthetic data for model pretraining. Conduct large‑scale pretraining with several datasets. Knowledge with relational database systems, SQL, or data‑warehouse architectures. Distributed training and large‑scale GPU experimentation. Benchmark development or contributions to open‑source machine learning libraries. Meet the Team SAP is uniquely positioned to lead the next wave of AI by infusing intelligence directly into the business processes that run the world. Our team's mission is to develop Foundation Models for structured data, starting with the launch and continued evolution of SAP‑RPT‑1. As the first generation of our relational model portfolio, SAP‑RPT‑1 represents our commitment to pioneering new research in a domain where SAP's expertise in enterprise data structures provides an unmatched competitive edge. We are looking for innovators to join us in this journey to develop next‑generation models with even higher impact, bridging the gap between cutting‑edge AI research and the complex, structured reality of global enterprise data. Equal Employment Opportunity (EEO) Statement Our company does not discriminate in employment on the basis of race, color, religion, sex (including pregnancy and gender identity), national origin, political affiliation, sexual orientation, marital status, disability, genetic information, age, membership in an employee organization, retaliation, parental status, military service, or other non‑merit factor. All your information will be kept confidential according to EEO guidelines. About SAP With a global network of customers, partners, employees, and thought leaders, SAP helps the world run better and improves people’s lives. As a leader in enterprise applications and business AI, SAP stands at the nexus of business and technology. For over 50 years, organizations have trusted SAP to bring out their best by uniting business‑critical operations spanning finance, procurement, HR, supply chain, and customer experience. We win with inclusion SAP’s culture of inclusion, focus on health and well‑being, and flexible working models help ensure that everyone – regardless of background – feels included and can run at their best. At SAP, we believe we are made stronger by the unique capabilities and qualities that each person brings to our company, and we invest in our employees to inspire confidence and help everyone realize their full potential. We ultimately believe in unleashing all talent and creating a better world. SAP is committed to the values of Equal Employment Opportunity and provides accessibility accommodations to applicants with physical and/or mental disabilities. If you are interested in applying for employment with SAP and are in need of accommodation or special assistance to navigate our website or to complete your application, please send an e‑mail with your request to Recruiting Operations Team: View email address on click.appcast.io. For SAP employees: Only permanent roles are eligible for the SAP Employee Referral Program, according to the eligibility rules set in the SAP Referral Policy. Specific conditions may apply for roles in Vocational Training. Qualified applicants will receive consideration for employment without regard to their age, race, religion, national origin, ethnicity, age, gender (including pregnancy, childbirth, et al), sexual orientation, gender identity or expression, protected veteran status, or disability. Compensation Range Transparency SAP believes pay transparency is essential to fostering an honest, supportive, and inclusive culture and is an important part of our commitment to pay equity. The salary range for this position is included in the job posting. The actual compensation offered to the successful candidate will fall within that range and will depend on factors such as education, skills, experience, location, and scope of the role, as determined through the selection process. Where applicable, SAP may also provide variable incentive opportunities and additional benefits, subject to the terms of the relevant plans and policies. Any variable incentive amount is target‑based, with actual payouts dependent on company and individual performance. Please reference this link for a summary of SAP benefits and eligibility requirements: SAP North America Benefits . AI Usage in the Recruitment Process For information on the responsible use of AI in our recruitment process, please refer to our Guidelines for Ethical Usage of AI in the Recruiting Process. Please note that any violation of these guidelines may result in disqualification from the hiring process. 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