About the Role
On the Servicing ML team, you will build and improve machine learning and AI systems that automate customer operations such as disputes, returns, fraud, and chargebacks to make the best decisions for Affirm and our customers. You will work closely with experienced ML engineers, platform partners, and cross-functional stakeholders to take models from idea to prototype to production, and to keep them healthy with strong measurement and monitoring.
What you'll do
- You will develop AI systems that automate dispute and chargeback handling using structured evidence and business logic, creating a better experience for our customers.
- You will build models that automate refunds, getting money back to our customers faster.
- You will build and maintain evidence extraction pipelines that process unstructured data using LLM-powered workflows to produce structured, actionable outputs.
- You will prototype new modeling ideas, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls.
- You will collaborate across Engineering, Servicing Operations, Product, and ML Platform to define requirements, evaluate tradeoffs, and communicate results clearly to both technical and non-technical audiences.
What we look for
- You have a total of 2+ years of experience as a machine learning engineer.
- Strong Python skills and experience writing production-quality code.
- Experience building and evaluating models for tabular classification problems (preferably gradient-boosted decision trees like LightGBM/XGBoost/CatBoost).
- Experience building applications with LLM APIs (e.g., OpenAI, Anthropic), including structured extraction, prompt engineering, and orchestration frameworks like LangChain or LangGraph.
- Familiarity with document and unstructured data processing (PDF/image extraction, text parsing, or similar).
- Experience with ML lifecycle tooling for training orchestration, experimentation, and model monitoring (e.g., Kubeflow, Airflow, MLflow, or equivalent internal platforms).
- Proficient in using AI-powered developer tools (e.g., Claude Code, Cursor, or similar) to accelerate iteration, debugging, and code quality as part of day-to-day development workflows.
- You have mastered taking a simple problem or business scenario into a solution that interacts with multiple software components, and executing on it by writing clear, easily understood, well tested and extensible code.
- You are comfortable navigating a large code base, debugging others' code, and providing feedback to other engineers through code reviews.
- Your experience demonstrates that you take ownership of your growth, proactively seeking feedback from your team, your manager, and your stakeholders.
- You have strong verbal and written communication skills that support effective collaboration with our global engineering team.