Senior AI Researcher – Relational Foundation Models

SAP IT Business Systeme; Palo Alto (CA); On-site; USD 150,000 - 306,000; Full time

Job summary

SAP is leading the next wave of AI by embedding intelligence into enterprise processes. We develop relational foundation models for structured data and will launch SAP‑RPT‑1 as the first in our portfolio, combining deep research with scalable deployment.

We seek innovators bridging cutting-edge AI and real business data to drive impact across functions. You will own research into RFMs, work on production-ready methods, and collaborate globally with teams while mentoring rising talent.

Qualifications

• PhD or equivalent demonstrated research in a related field.
• Strong publication record at top venues (NeurIPS, ICML, ICLR, KDD, AAAI, AISTATS).
• Deep expertise in modern foundation-model techniques including transformers and attention mechanisms.
• Hands-on experience in tabular ML, graph neural networks, relational learning, or structured prediction.

Responsibilities

• Lead original research into Relational Foundation Models and architectures.
• Design and develop novel model architectures, pretraining strategies, learning objectives, and inference methods for relational data.
• Run large-scale distributed GPU experiments; build robust baselines and evaluation benchmarks.
• Contribute datasets, synthetic data generators, and evaluation frameworks for relational learning.
• Translate research findings into scalable prototypes for engineering and product teams.
• Mentor researchers and engineers and provide technical leadership on projects.
• Publish at top-tier venues and engage with the AI research community.
• Stay current with advances in foundation models, tabular learning, graph learning, and large-scale ML.

Skills

Transformer architectures

Attention mechanisms

Python

PyTorch

Independent research

Education

PhD in Machine Learning/CS/Statistics/Applied Mathematics

Tools

Research frameworks

Job description

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.

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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

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Please note that any violation of these guidelines may result in disqualification from the hiring process.

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