Staff/Principal Machine Learning Engineer (Modeling), Afterpay Risk

Block is a company focused on empowering customers and retailers through innovative financial solutions. They are seeking a Staff/Principal Machine Learning Engineer to architect and build AI/ML systems for fraud prevention and abuse mitigation, collaborating with cross-functional teams to enhance decision-making processes and improve the lending ecosystem.

Responsibilities:

• Architect and build AI/ML systems that power fraud prevention and abuse mitigation across the Afterpay lending lifecycle
• Shape lending decision frameworks with advanced analytics, machine learning, and automation to improve precision, adaptability, and speed
• Analyze large and complex datasets to surface insights that influence underwriting strategy, strengthen ecosystem safeguards, and inform product direction
• Lead signal discovery and deep experimentation to identify evolving risk patterns and inform the design of next-generation decision systems
• Build agentic features for operations teams that surface emerging fraud vectors, adapt to changing patterns, and enable faster decision-making at scale
• Build agentic engineering workflows that accelerate development, testing, and documentation
• Collaborate cross-functionally with Product, Engineering, and Operations to design systems and features that enhance trust and portfolio health
• Share modeling context and approaches across teams, helping align how fraud risk is measured, interpreted, and discussed
• Mentor and elevate fellow scientists and modelers, contributing to modeling standards, best practices, and technical excellence across the team
• Exercise a high level of autonomy and ownership, driving solutions from problem framing and design through deployment and iteration

Requirements:

• A Bachelor's degree in a quantitative field (e.g., Mathematics, Statistics, Physics, Computer Science). Advanced degrees preferred
• 10+ years of experience in fraud, risk, credit underwriting, or another high-stakes decisioning domain
• Deep expertise in AI and machine learning methods, statistical modeling, and advanced analytical techniques
• Strong experimentation skills: you know how to design holdouts, measure lift, and evaluate models beyond aggregate metrics
• Strong analytical rigor with a deep testing mindset, including experience designing automated validation frameworks for models and decision logic
• Proficiency with AI-native development workflows. You use LLMs, agentic coding tools, and AI-assisted automation as a regular part of how you build and ship
• Experience explaining modeling concepts, results, and limitations to senior stakeholders and cross-functional partners
• Experience working across disciplines in environments with meaningful constraints