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.
Requirements
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.
This position requires either equivalent practical experience or a Bachelor’s degree in a related field
Tech Stack
Airflow
Python
Benefits
Health care coverage
Affirm covers all premiums for all levels of coverage for you and your dependents
Flexible Spending Wallets
generous stipends for spending on Technology, Food, various Lifestyle needs, and family forming expenses
Time off
competitive vacation and holiday schedules allowing you to take time off to rest and recharge
ESPP
An employee stock purchase plan enabling you to buy shares of Affirm at a discount