Staff Machine Learning Engineer (Modeling), Credit

Block is a company that started with the vision of redefining the world's relationship with money and has developed various financial products including Cash App. The Staff Machine Learning Engineer will be responsible for building and evolving machine learning systems that underpin credit products, focusing on model development and decision frameworks.

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