Machine Learning Engineer II - AI

Join Adyen’s Payments organization as a Machine Learning Engineer II - AI/ML, helping build and scale agentic AI systems for complex operational challenges in a global payments environment.

This is Adyen

Adyen provides payments, data, and financial products in a single solution for customers like Meta, Uber, H&M, and Microsoft. Our Payment Solutions team processes billions of transactions while enabling businesses to create seamless payment experiences. As Adyen and our merchants scale globally, the complexity and volume of operational knowledge continue to grow.

Software Engineer II AI/ML - Payments

To position the Payments organization at the technological forefront, we are establishing a GenAI team focused on identifying high-impact use cases for automation through agentic capabilities. As a Machine Learning Engineer II - AI/ML, you will undertake technically demanding applied AI work: designing agents that reason over complex, multi-step tasks, building infrastructure for production-grade reliability, and shaping how humans and AI collaborate at scale within a global payments company.

This is not a narrow research role. You will take full ownership of your work, from early research through deployed production systems, influence the team’s technical direction, and act as a force multiplier for the broader AI organization, including contributing to custom model development for structured financial data.

What you’ll do:

• Discover and build by proactively engaging with product and engineering teams to uncover critical challenges, identify high-impact opportunities, and rapidly design AI prototypes (MVPs) to demonstrate value.
• Develop strategic AI products by owning the end-to-end development of bespoke AI tools that solve problems unique to Adyen’s scale, including merchant experience, pricing models, and internal workflows.
• Own evaluation and benchmarking by defining and leading evaluation strategies for agentic systems and LLMs, designing internal benchmarks grounded in real domain complexity, and building reusable evaluation infrastructure embedded in development.
• Provide AI expertise across the organization by serving as a technical resource for AI initiatives, evaluating frameworks, retrieval and search strategies, and agent tool-use approaches across partner teams.
• Raise the bar by setting engineering standards, mentoring through problem decomposition, research methodology, and code review, and championing reproducibility, documentation, and rigorous evaluation practices.

Who you are:

• You have 3-6 years of hands-on experience in applied AI/ML research or engineering, with a track record of shipping AI systems, including agentic or LLM-powered systems, in production environments.
• You have deep expertise in language models and Generative AI, with hands-on depth across areas such as architecture, post-training (fine-tuning, RLHF), inference optimization, context engineering (RAG), and failure modes at scale.
• You have 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.
• You are rigorous and systematic about evaluation, with experience designing evaluation frameworks or internal benchmarks that go beyond standard metrics.
• You have a strong foundation in classical machine learning, including supervised learning, ensemble methods, optimization, probabilistic modeling, and statistics.
• You write clean, well-structured, production-ready code, primarily in Python, and hold research code to an engineering standard.
• You have hands-on experience with at least one production-grade agentic framework.

Nice to Have

• Familiarity with financial data, payments, fraud detection, or risk systems.
• Track record of external visibility through publications, conference presentations, or open-source contributions.
• Experience with observability and evaluation tooling.
• Familiarity with MLOps and model deployment pipelines in large-scale environments.

This role is based out of our Amsterdam office. We are an office-first company and value in-person collaboration; we do not offer remote-only roles.