Machine Learning Engineer II (Servicing ML)

Affirm is hiring a Machine Learning Engineer II for its Servicing ML team, working remotely in Canada. The team builds and operates machine learning systems that automate customer operations including disputes, returns, fraud, and chargebacks. You will collaborate with ML engineers, platform partners, and cross-functional teams to develop, deploy, and maintain production models that balance business objectives with customer outcomes.

Responsibilities

• Develop AI systems that automate dispute and chargeback handling using structured evidence and business logic
• Build models that automate refund decisions and accelerate money return to customers
• Construct and maintain evidence extraction pipelines that process unstructured data using LLM-powered workflows to generate structured, actionable outputs
• Prototype 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 tradeoffs, and communicate findings to technical and non-technical stakeholders

Requirements

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