Staff/Principal Machine Learning Engineer (Modeling), Credit

It all started with an idea at Block in 2013. Initially built to take the pain out of peer-to-peer payments, Cash App has gone from a simple product with a single purpose to a dynamic ecosystem, developing unique financial products, including Afterpay/Clearpay, to provide a better way to send, spend, invest, borrow and save to our 50+ million monthly active customers. We want to redefine the world's relationship with money to make it more relatable, instantly available, and universally accessible.

Today, Cash App has thousands of employees working globally across office and remote locations, with a culture geared toward innovation, collaboration and impact. We've been a distributed team since day one, and many of our roles can be done remotely from the countries where Cash App operates. No matter the location, we tailor our experience to ensure our employees are creative, productive, and happy.

The Role

Block has provided over $200 billion in credit to customers globally. Afterpay and Cash App Borrow are our two largest products in this space, expanding access to credit for consumers who are often underserved by traditional financial systems. Machine learning is the core of how these products work.

Our models decide who gets credit, how much, and under what terms. They underwrite customers across a wide range of credit profiles, including many with thin or no traditional credit history. The modeling challenges are real: maintaining calibration across diverse borrower populations, designing features that generalize as the portfolio grows, and balancing approval rates against loss performance at every decision point. This requires strong fundamentals, disciplined experimentation, and continuous evaluation in production.

On the Credit Modeling team, you will be a senior individual contributor building and evolving the ML systems behind these products. You will work across the full modeling lifecycle: problem formulation, feature development, training, calibration, experimentation, deployment, monitoring, and iteration. You will operate across two distinct lending products with different borrower populations, repayment structures, and regulatory surfaces.

We use agentic engineering and AI tooling to build reliable, high-velocity workflows that enable this work. That includes code generation, automated testing, documentation, and developer tooling. You will help define how these practices scale across the team in ways that are rigorous, auditable, and trusted.

This is a team that values high output and rigor. We move fast, we test carefully, and we hold our work to a high standard because the models we build determine real credit outcomes for real people.

This role is fully remote for candidates based in the US or Canada.

You Will

• 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.

You Have

• 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.

Technologies We Use and Teach

• 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