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Adyen is a global financial technology platform processing billions of transactions for enterprises like Meta, Uber, H&M, and Microsoft. The Payments Solutions team is establishing a GenAI team to identify and implement high-impact use cases for automation through agentic AI capabilities.
As a Senior Machine Learning Engineer - AI, you will own technically demanding applied AI work across the full lifecycle: designing agents that reason over complex, multi-step tasks; building production-grade infrastructure for reliability; and shaping human-AI collaboration at scale within a global payments company. This is not a narrow research role—you will take full ownership from early research through deployed production systems, influence technical direction, and act as a force multiplier for the broader AI organization.
Key responsibilities include:
**Discover and Build:** Proactively engage with product and engineering teams to uncover critical challenges. Rapidly design and build AI prototypes (MVPs) to demonstrate value without waiting for instructions.
**Develop Strategic AI Products:** Own end-to-end development of bespoke AI tools solving problems unique to Adyen's scale—optimizing merchant experience, enhancing pricing models, improving internal workflows, and contributing to custom model development for structured financial data.
**Own Evaluation and Benchmarking:** Define and lead evaluation strategy for agentic systems and LLMs. Design internal benchmarks grounded in real domain complexity, probe for genuine capabilities and edge cases, and build reusable evaluation infrastructure embedded in the development process.
**Provide AI Expertise Across the Organization:** Serve as a technical resource evaluating agentic frameworks, retrieval strategies, and agent tool-use approaches. Surface connections across initiatives and help teams avoid duplicating work.
**Raise the Bar:** Set engineering standards for the team and company. Provide mentorship through problem decomposition, research methodology, and code review. Champion reproducibility, documentation, and rigorous evaluation practices.
Required qualifications: 7+ years of hands-on applied AI/ML research or engineering with a track record of shipping AI systems (including agentic or LLM-powered systems) in production. Deep expertise in language models and generative AI across architecture, post-training (fine-tuning, RLHF), inference optimization, context engineering (RAG), and failure modes at scale. Proven experience designing and operating agentic systems at scale, including multi-agent orchestration, tool use, memory and context management, state handling for long-running workflows, and human-in-the-loop design. Rigorous and systematic about evaluation with experience designing evaluation frameworks beyond standard metrics. Strong foundation in classical machine learning (supervised learning, ensemble methods, optimization, probabilistic modeling, statistics). Clean, production-ready code primarily in Python. Hands-on experience with at least one production-grade agentic framework.
Nice to have: Familiarity with financial data, payments, fraud detection, or risk systems; external visibility (publications, conferences, open-source); experience with observability and evaluation tooling; MLOps and model deployment pipelines in large-scale environments.
