Description
Adyen is building a top-tier AI engineering organization in Amsterdam, San Francisco, and Madrid to drive innovation using AI globally. As a Senior AI Research Engineer, you will operate with a high degree of autonomy and responsibility, delivering strategic, high-impact outcomes that bridge the gap between advanced AI research and production-grade applications at a global scale.
Responsibilities:
- Innovate and Deploy: Drive the execution of Adyen's AI strategy, focusing on the practical application of Generative AI (GenAI) and other AI methodologies in finance.
- Build Production-grade Applications: Bridge the gap between cutting-edge AI research and production by implementing research papers into robust, scalable, and production-ready code.
- Optimize and Scale: Contribute to defining the long-term vision for AI at Adyen, specifically how AI will interact with humans and finance.
- Think Outside the Box: Drive innovation by challenging the status quo, introducing transformative ideas, and implementing creative solutions to solve real-world problems.
- Force Multiplier: Provide mentorship and horizontal sponsorship across the organization, fostering collaboration to share knowledge and best practices.
- Team Player: Actively pair with other engineering teams to solve deep-rooted technical challenges.
- Learn and Lead: Connect with the broader AI community to stay informed of the latest advancements and identify potential partnership opportunities.
Requirements:
- You are deeply embedded in the scientific AI research community and have a strong understanding of the latest SOTA advancements.
- You have significant experience and a strong understanding of Generative AI (GenAI) and Large Language Models (LLMs).
- You demonstrate a strong engineering mindset with a track record of writing clean, efficient, and scalable code suitable for production environments.
Nice to Haves:
- A strong product sense and the ability to identify impactful AI use cases.
- Experience in AI-enabled fintech companies.
- Familiarity with classical machine learning concepts.
- Experience with external visibility activities such as conferences and publications.
- Experience working with on-premise infrastructure.