Machine Learning Engineer II

Affirm Affirm · Fintech · Canada, United States · Remote · Checkout

Machine Learning Engineer II at Affirm to build and improve AI systems for automating customer operations like disputes, returns, fraud, and chargebacks. The role involves developing AI systems using LLM-powered workflows, building models for refunds, and creating evidence extraction pipelines. It requires experience in production ML, LLM APIs, and ML lifecycle tooling.

What you'd actually do

  1. You will develop AI systems that automate dispute and chargeback handling using structured evidence and business logic, creating a better experience for our customers.
  2. You will build models that automate refunds, getting money back to our customers faster.
  3. You will build and maintain evidence extraction pipelines that process unstructured data using LLM-powered workflows to produce structured, actionable outputs.
  4. You will prototype new modeling ideas, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls.
  5. 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.

Skills

Required

  • 2+ years of experience as a machine learning engineer
  • Strong Python skills
  • Experience building and evaluating models for tabular classification problems
  • Experience building applications with LLM APIs
  • Familiarity with document and unstructured data processing
  • Experience with ML lifecycle tooling
  • Experience taking a simple problem or business scenario into a solution that interacts with multiple software components
  • Comfortable navigating a large code base, debugging others' code, and providing feedback to other engineers through code reviews

Nice to have

  • gradient-boosted decision trees like LightGBM/XGBoost/CatBoost
  • structured extraction
  • prompt engineering
  • orchestration frameworks like LangChain or LangGraph
  • PDF/image extraction, text parsing, or similar
  • Kubeflow, Airflow, MLflow, or equivalent internal platforms
  • AI-powered developer tools (e.g., Claude Code, Cursor, or similar)

What the JD emphasized

  • production-quality code
  • building applications with LLM APIs
  • evidence extraction pipelines
  • LLM-powered workflows

Other signals

  • LLM-powered workflows
  • automating customer operations
  • models from idea to production