Senior Machine Learning Engineer - Fraud
Leads applied research for next-generation fraud detection models across graph, sequential, image, and video data, translating prototypes into production solutions. Requires strong Python skills, research leadership, and a PhD or equivalent research experience.
About the job
Responsibilities
- Research and prototype state-of-the-art approaches in graph machine learning, sequential modeling, and multimodal learning for fraud detection.
- Own and execute a research roadmap, translating innovative ideas and prototypes into production solutions with measurable product and customer impact.
- Design rigorous experiments and evaluation methodologies reflecting real-world fraud dynamics.
- Collaborate with Machine Learning Engineers, Data Scientists, Product, and Engineering teams to transition research into production.
- Leverage network-level financial data to uncover fraud signals and develop detection solutions.
- Publish and share applied research internally and externally.
Requirements
- PhD in Machine Learning, Artificial Intelligence, Computer Science, Statistics, Applied Mathematics, or a closely related field strongly preferred; equivalent research experience may be considered.
- 2–4+ years of relevant industry or research-lab experience, ideally post-PhD.
- Demonstrated research leadership and a record of translating innovative research into measurable product or business impact.
- Strong scientific rigor and written and verbal communication skills.
- Strong proficiency in Python.
- Experience building high-quality research prototypes that can inform or transition into production systems.
Nice-to-haves
- Experience in fraud detection, security, risk, or abuse prevention.
- Experience with large-scale training, graph systems, and sequential modeling.
Compensation and benefits
- Annual salary range of $228,960–$315,360.
- Additional compensation may include equity and/or commission.
- Comprehensive benefits including medical, dental, vision, and 401(k).
Skills
Python, Machine Learning, Graph Machine Learning, Graph Neural Networks, Transformers, Foundation Models, Sequential Modeling, Multimodal Learning, Fraud Detection, Graph Systems, Large-Scale Training, Model Serving, Experiment Design, Data Science
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