Machine Learning Engineer II (Servicing ML)

Affirm is hiring a Machine Learning Engineer II for its Servicing ML team (remote, US-based). You'll develop and maintain ML and AI systems that automate customer operations including disputes, returns, fraud, and chargebacks. This role involves taking models from conception through production deployment, collaborating with ML engineers, platform teams, and cross-functional partners to ensure systems remain reliable and measurable.

Responsibilities

• Develop AI systems that automate dispute and chargeback handling using structured evidence and business logic
• Build models that automate refund processing to accelerate customer reimbursements
• Build and maintain evidence extraction pipelines that process unstructured data using LLM-powered workflows to generate structured, actionable outputs
• Prototype new modeling approaches, conduct offline experiments, and deploy high-performing solutions into production with appropriate risk controls
• Collaborate with Engineering, Servicing Operations, Product, and ML Platform teams to define requirements, evaluate design tradeoffs, and communicate results to technical and non-technical stakeholders

Requirements

• 2+ years of experience as a machine learning engineer
• Strong Python proficiency and ability to write production-grade code
• Experience building and evaluating tabular classification models, particularly with gradient-boosted decision trees (LightGBM, XGBoost, CatBoost, or equivalent)
• Experience building applications with LLM APIs (OpenAI, Anthropic) including structured extraction, prompt engineering, and orchestration frameworks (LangChain, LangGraph, or similar)
• Familiarity with document and unstructured data processing (PDF/image extraction, text parsing, or related techniques)
• Experience with ML lifecycle and monitoring tools (Kubeflow, Airflow, MLflow, or equivalent platforms) for training orchestration, experimentation, and model monitoring
• Proficiency with AI-powered developer tools (Claude Code, Cursor, or similar) as part of regular development workflows
• Demonstrated ability to translate business scenarios into multi-component software solutions with clear, well-tested, and maintainable code
• Ability to navigate large codebases, debug others' code, and contribute effectively to shared systems