Staff Machine Learning Engineer, Underwriting and Credit

Block is a technology company focused on increasing access to the global economy through its ecosystem, including Square and Cash App. They are seeking a Staff Machine Learning Engineer to build and evolve ML systems for underwriting and credit decisioning, working across the full modeling lifecycle to enhance credit offerings for underserved consumers.

Responsibilities:

• Build, evaluate, and maintain underwriting and decisioning models across Cash App Borrow and Afterpay
• Design and evolve credit decision frameworks, including the modeling, automation, and policy logic that manage credit exposure over time
• Design and run experiments to evaluate model performance, measure impact on approval rates and loss, and inform credit policy decisions
• Develop deep understanding of borrower behavior, repayment dynamics, and portfolio structure across both products, and use that to inform model design and decision logic
• Contribute analysis and perspective that inform portfolio-level decisions, including explaining model behavior, tradeoffs, and uncertainty to senior technical and business leaders
• Work across the full modeling lifecycle: problem formulation, feature engineering, training, calibration, deployment, monitoring, and iteration in production
• Build agentic engineering workflows that accelerate development, testing, and documentation
• Collaborate with Product, Engineering, Legal, Compliance, and Operations to ensure credit systems reflect business goals and regulatory expectations
• Share modeling context and approaches across teams, helping align how credit risk is measured, interpreted, and discussed
• Shape how AI developer tooling is adopted across the team, defining review practices, quality standards, and governance patterns

Requirements:

• A Bachelor's degree in a quantitative field (e.g., Mathematics, Statistics, Physics, Computer Science). Advanced degrees welcome
• 10+ years applying AI, machine learning, or statistical modeling in decisioning contexts such as credit, risk, fraud, recommendations, or similar domains
• Experience with probabilistic models and decision systems, including calibration, score transformations, and interpretation of model outputs
• Strong experimentation skills: you know how to design holdouts, measure lift, and evaluate models beyond aggregate metrics
• Experience with model monitoring, degradation detection, and retraining strategies in production systems
• 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