Machine Learning Engineer II (Servicing ML)

About the role

Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest. 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.

Responsibilities

• Develop AI systems that automate dispute and chargeback handling using structured evidence and business logic, creating a better experience for our customers.
• Build models that automate refunds, getting money back to our customers faster.
• Build and maintain evidence extraction pipelines that process unstructured data using LLM-powered workflows to produce structured, actionable outputs.
• Prototype new modeling ideas, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls.
• 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

• 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.

Qualifications

• Total of 2+ years of experience as a machine learning engineer.
• Experience 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.

Skills

• Python programming skills.
• Experience with LLM APIs (e.g., OpenAI, Anthropic).
• Experience 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).
• Experience with AI-powered developer tools (e.g., Claude Code, Cursor, or similar).

Benefits

• Base Pay Grade - L
• Equity Grade - 6
• Remote-first company
• Competitive benefits package including health care coverage, flexible spending wallets, time off, ESPP, and more