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.

IDEAL QUALIFICATIONS

• 6+ years of professional experience in machine learning engineering, software engineering, or applied research roles.
• Deep proficiency in Python and familiarity with Go/TypeScript; experience with distributed systems and orchestration frameworks.
• Strong background in ML/AI, including large language models, embeddings, RAG, and/or multimodal generation.
• Hands-on experience with cloud-native infrastructure (Kubernetes, AWS/Azure) and modern data stores (Postgres, Cassandra, vector DBs).
• Proven track record of delivering production AI systems at scale.
• Strong problem-solving skills and ability to balance innovation with enterprise-grade reliability.
• Experience mentoring engineers and shaping engineering culture.

WHAT YOU’LL DO

As a Senior/Staff Machine Learning Engineer, you’ll be a technical leader helping architect, scale, and harden SAP’s next generation of generative AI services. You’ll drive both design and implementation of ML pipelines, orchestrators, and agentic workflows—turning foundational model research into secure, production-ready enterprise solutions

KEY RESPONSIBILITIES

• Architect and optimize ML-driven workflows for multi-agent orchestration (LangGraph) across DRP, Experience Builder, and Visual Services.
• Lead technical design discussions, mentor junior engineers, and set engineering standards for reliability and scale.
• Evaluate, fine-tune, and integrate foundation models (text, multimodal, diffusion) through SAP’s GenAI Hub and third-party providers (e.g., Amazon Bedrock).
• Design evaluation frameworks and dashboards for quality, safety, and enterprise trust in generative outputs.
• Partner with product teams to define roadmaps and deliver features that serve Sales, Marketing, Finance, HR, and beyond.
• Collaborate with cross-functional SAP AI teams on shared infrastructure, governance, and interoperability.
• Stay at the forefront of ML/AI trends and bring best practices into the team’s architecture and execution.