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About This Role
Company Description
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.
Job Description
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.
Qualifications
architectural thinking and hands-on coding skills. You design secure, scalable systems, reason about data structures and APIs, and use observability, CI/CD and automated testing to ship safely and fast. You can dive deep on performance tuning, apply secure-by-default patterns, and mentor others in writing clean, maintainable code.
Additional Information
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: [email protected].
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.
Salary Context
This $148K-$306K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At SAP IT Business Systeme, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($227K) sits 7% above the category median. Disclosed range: $148K to $306K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
SAP IT Business Systeme AI Hiring
SAP IT Business Systeme has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Consultant. Positions span Palo Alto, CA, US, Herndon, VA, US. Compensation range: $198K - $420K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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