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About This Role
We help the world run better
At SAP, we keep it simple: you bring your best to us, and we'll bring out the best in you. We're builders touching over 20 industries and 80% of global commerce, and we need your unique talents to help shape what's next. The work is challenging – but it matters. You'll find a place where you can be yourself, prioritize your wellbeing, and truly belong. What's in it for you? Constant learning, skill growth, great benefits, and a team that wants you to grow and succeed. What You’ll Build
We are seeking a highly motivated and curious Developer Associate to join the SuccessFactors Tools \& Process Team, a central function driving large\-scale transformation across HCM Product \& Engineering and Strategy \& Operations.
This role sits at the intersection of AI innovation, enterprise tooling, and developer productivity, supporting strategic initiatives such as AI\-powered solutions and Developer Experience (DevEx) improvements across multiple Lines of Business.
In this role, you will contribute to the design and development of AI\-driven solutions, intelligent integrations, and automation frameworks across key platforms including Jira, Aha!, ServiceNow, and SharePoint. You will collaborate closely with program leaders, AI engineers, and cross\-functional product teams to deliver enterprise\-grade solutions that improve operational efficiency, enable data\-driven insights, and enhance developer workflows.
*Key Responsibilities*
- Develop and support AI\-driven use cases and prototypes (e.g., intelligent search, automation, analytics, and insights) for HCM Product \& Engineering and COO teams
- Build AI agents, MCP servers, and applications for process and workflow automation, improving developer workflow productivity and developer experience
- Build integrations across enterprise tools (Jira, Aha!, ServiceNow, SharePoint) using APIs, connectors, multi\-tool AI agents, and middleware frameworks
- Design and implement automation workflows to improve operational efficiency and reduce manual effort across teams
- Develop AI/LLM\-based applications and agents leveraging enterprise AI platforms and tools (e.g., Azure OpenAI, Cline, Claude Code, and GitHub Copilot)
- Improve Developer Experience (DevEx) by enhancing workflows, tooling, and CI/CD or automation capabilities
- Collaborate with cross\-functional stakeholders to gather requirements, prototype solutions, and deliver scalable implementations
- Maintain technical documentation, reusable components, and best practices to support long\-term scalability
What You Bring
*Required Skills \& Qualifications*
- Bachelor’s or Master’s degree in Computer Science, Information Systems, Engineering, or related field
- 1–3 years of experience (or strong academic/internship experience) in software development or AI\-driven automation
- Experience with AI frameworks, prompt engineering, or building AI copilots/agents
- Proficiency in one or more programming languages such as Python, JavaScript/Node.js, or Java
- Experience working with APIs, REST services, and system integrations
- Experience with developing RAG applications, data vectorization, and working with Databricks
- Experience in AI tools like Claude Code, Cline, GitHub Copilot
- Familiarity with enterprise tools such as Jira, ServiceNow, Aha!, or similar platforms
- Basic understanding of AI/ML concepts or LLM\-based applications
- Knowledge of cloud platforms (Azure, AWS, or SAP BTP)
- Strong problem\-solving skills and ability to work in a fast\-paced, cross\-functional environment
- Good communication skills with the ability to translate business needs into technical solutions
*Nice to Have (Preferred)*
- Exposure to DevOps practices, CI/CD pipelines, or developer tooling improvements
- Experience with data processing, analytics, or dashboarding tools (e.g., Power BI)
- Familiarity with Agile methodologies and product engineering environments
What You’ll Gain
- Hands\-on experience working on enterprise\-scale AI and tooling transformation initiatives
- Opportunity to contribute to high\-visibility programs such as AI adoption across HCM
- Exposure to cutting\-edge AI technologies and real\-world enterprise use cases
- Collaboration with senior leaders, architects, and cross\-functional global teams
- A platform to learn, innovate, and grow within SAP’s product and engineering ecosystem
Bring out your best
SAP innovations help more than four hundred thousand customers worldwide work together more efficiently and use business insight more effectively. Originally known for leadership in enterprise resource planning (ERP) software, SAP has evolved to become a market leader in end\-to\-end business application software and related services for database, analytics, intelligent technologies, and experience management. As a cloud company with two hundred million users and more than one hundred thousand employees worldwide, we are purpose\-driven and future\-focused, with a highly collaborative team ethic and commitment to personal development. Whether connecting global industries, people, or platforms, we help ensure every challenge gets the solution it deserves. At SAP, you can bring out your best.
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: Careers@sap.com.
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 the value of pay transparency contributes towards an honest and supportive culture and is a significant step toward demonstrating SAP’s commitment to pay equity. SAP provides the annualized compensation range inclusive of base salary and variable incentive target for the career level applicable to the posted role. The targeted annual combined range for this position is 67900\-153900USD. The actual amount to be offered to the successful candidate will be within that range, dependent upon the key aspects of each case which may include education, skills, experience, scope of the role, location, etc. as determined through the selection process. Any SAP variable incentive includes a targeted dollar amount and any actual payout amount is dependent on company and personal 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.
Requisition ID: 450598 \| Work Area: Software\-Design and Development \| Expected Travel: 0 \- 10% \| Career Status: Graduate \| Employment Type: Regular Full Time \| Additional Locations: \#LI\-Hybrid
Salary Context
This $67K-$153K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At SAP, 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 $166,983 based on 13,781 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $76,880. This role's midpoint ($110K) sits 34% below the category median. Disclosed range: $67K to $153K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
SAP AI Hiring
SAP has 44 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Architect, Data Engineer. Positions span Palo Alto, CA, US, Newtown Square, PA, US, Chicago, IL, US. Compensation range: $128K - $435K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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