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Block, Inc.
Staff Machine Learning Engineer, Underwriting and Credit
Block, Inc.
Anywhere•Remote••5-8 Yrs•1 day ago
Job description
Remote
Overview
Block builds technology to increase access to the global economy. Across our ecosystem, including Square and Cash App, we create tools that help businesses run and grow, and individuals move, manage, and grow their money with confidence.
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.
- 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.
Qualifications
- Strong experimentation skills: you know how to design holdouts, measure lift, and evaluate models beyond aggregate metrics.
- 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.
- Python (NumPy, Pandas, scikit-learn, PyTorch, XGBoost, LightGBM)
- AI development tools as core infrastructure: Claude Code, Cursor, Copilot
- MLflow for experiment tracking and model registry
- Internal feature store and model hosting platform
- Prefect and Airflow for orchestration
- SQL / Snowflake
- GitHub
- GCP / AWS