Staff Applied Machine Learning Engineer - Fraud & Abuse
Staff Applied ML Engineer building and operating production ML decision systems to detect and prevent payment fraud, scams, identity abuse, and marketplace risk across Block.
Staff Machine Learning Engineer, Underwriting and Credit
Senior IC building and maintaining ML underwriting and credit decisioning models for Cash App Borrow and Afterpay. Owns full modeling lifecycle including experimentation, calibration, deployment, and monitoring.
277k – 415kUnited StatesML EngineeringRemote10+ YOE
About the role
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
- 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: 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 using LLMs, agentic coding tools, and AI-assisted automation.
- Experience explaining modeling concepts, results, and limitations to senior stakeholders and cross-functional partners.
- Experience working across disciplines in environments with meaningful constraints.
Technologies
- Python (NumPy, Pandas, scikit-learn, PyTorch, XGBoost, LightGBM)
- AI development tools: 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
Skills
PythonNumPypandasscikit-learnPyTorchXgboostLightgbmMLflowPrefectAirflowSQLSnowflakeGCPAWS
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