Machine Learning Engineer II (Fraud)

About This RoleAI processing…

Key Responsibilities

• 1
  You will develop and iterate on fraud prediction models using a mix of approaches for tabular and behavioral data
• 2
  You will build and scale feature pipelines and training datasets from proprietary and third-party signals, partnering with data and platform teams when needed.
• 3
  You will prototype new modeling ideas and features, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls.
• 4
  You will help productionize models: integrate into batch and/or real-time decision systems, and improve reliability, latency, and operational robustness.
• 5
  You will instrument and monitor model and data health, and help define retraining/backtesting workflows as fraud patterns evolve.
• 6
  You will collaborate across Engineering, Fraud Analytics, Product, and ML Platform to define requirements, evaluate tradeoffs, and communicate results clearly to both technical and non-technical audiences.

Requirements

• 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
• You have a total of 2+ years of experience as a machine learning engineer or a PhD in a relevant field.
• 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, or similar).
• Experience with a deep learning framework (PyTorch preferred).
• Experience working with distributed data processing or parallel compute frameworks (Spark preferred; Ray/Dask 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.

Perks & Benefits