Sr.AI​/ML Fraud Engineer

The Sr. AI Applied Scientist (Fraud Prevention): drives experimental research and novel algorithm development to detect and predict emerging fraud trends. The candidate has an innovative approach with passion for fraud detection and prevention along with creativity in data visualization. The scientist will work with different partners across Adobe to develop outstanding fraud detection capabilities.

• Develop LLM Agentic Workflow and ML models to predict and detect fraud from historical and emerging trends across various platforms and products.
• Automate manual workflows with AI to build efficiencies and scale up fraud reviews, investigations, and operational workflows.
• Translate investigations and intel into root cause analysis to find opportunities for scale.
• Lead experimental research to invent novel ML algorithms and LLM-based agentic workflows for fraud detection, pushing the state-of-the-art in the field.
• Design and validate new automation paradigms using state-of-the-art AI, driving the transfer of these research innovations into scalable operational processes.
• Conduct rigorous data analysis and experimentation to identify novel patterns, contributing to the scientific community through publications and patents.

• Ph.D. in Computer Science, Statistics, Mathematics, or related quantitative field is preferred. A Master's degree with strong evidence of research activity (e.g., scientific publications in conferences like NeurIPS, ICML, AAAI, CVPR, IJCAI, ACL etc, patents, or equivalent applied research in industry) is also acceptable.
• 6+ years of proven experience in data science, machine learning, with a focus on fraud detection or risk management in subscription-based businesses or financial services.
• Expertise in AI and ML techniques, including LLM API, Agentic Framework, supervised and unsupervised learning, anomaly detection, and ensemble methods.
• Proficiency in programming languages such as SQL, Python or R. Strong understanding of big data technologies and distributed computing frameworks (e.g., Hadoop, Spark) for handling large-scale datasets.
• Deep research experience in Large Language Models (LLMs) and Agentic Workflows, with a proven track record of translating theoretical research into production-grade systems.
• Excellent communication and leadership skills, with the ability to collaborate effectively with cross-functional teams and drive alignment on fraud detection strategies.
• Proven track record of leading complex AI/ML projects from conception to production deployment, delivering tangible business value.
• Experience with fraud detection tools and platforms (e.g., fraud detection APIs, fraud management systems) is highly recommended.

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