Senior Machine Learning Engineer (Research Scientist), Fraud

What you'll do

• Research and prototype state-of-the-art approaches in graph machine learning, sequential modeling, and multimodal learning.
• Own and execute a research roadmap to translate innovative prototypes into production-ready fraud detection solutions.
• Design rigorous experiments and evaluation methodologies that reflect real-world fraud dynamics.
• Explore and evaluate state-of-the-art approaches such as Graph Neural Networks and Transformer-based foundation models.
• Leverage network-level financial data to uncover new signals and insights for fraud prevention.
• Partner with Machine Learning Engineers to transition research findings into production systems with measurable impact.
• Publish and share applied research findings to advance the technical bar for fraud machine learning.

What we're looking for

• PhD in Machine Learning, AI, Computer Science, Statistics, Applied Mathematics, or a related field (preferred).
• Equivalent research experience including significant publications, patents, or widely adopted research contributions (preferred).
• 2–4+ years of relevant industry or research lab experience, ideally post-PhD.
• Demonstrated research leadership and a track record of translating innovative research into measurable product or business impact.
• Strong proficiency in Python and experience building high-quality research prototypes for production systems.
• Demonstrated scientific rigor with strong written and verbal communication skills to communicate complex research findings.
• Experience in fraud detection, security, risk, or abuse prevention (preferred).
• Experience with large-scale training, graph systems, and sequential modeling (preferred).