Senior Machine Learning Engineer (Fraud)

Affirm is seeking a Senior Machine Learning Engineer to join its Fraud team, working remotely from Canada. You'll develop and maintain machine learning systems that power real-time fraud detection and transaction decisions, protecting both consumers and merchants while optimizing for fraud loss, customer experience, and conversion rates. This role involves collaborating with ML engineers, platform teams, and cross-functional partners to move models from concept through production and ongoing maintenance as fraud patterns shift.

Responsibilities

• Lead development of new fraud prediction models using tabular, graph, and behavioral data approaches
• Build and scale feature pipelines and training datasets from proprietary and third-party data sources, coordinating with data and platform teams
• Prototype modeling ideas and features, conduct offline experiments, and transition high-performing approaches into production with appropriate risk controls
• Productionize models: integrate into batch and real-time decision systems; optimize for reliability, latency, and operational robustness
• Instrument and monitor model and data health; establish retraining and backtesting workflows as fraud patterns evolve
• Identify and drive foundational improvements to the team's model-building processes
• Collaborate with Engineering, Fraud Analytics, Product, and ML Platform teams to define requirements, evaluate tradeoffs, and communicate findings to technical and non-technical stakeholders

Requirements

• 6+ years of experience researching, training, tuning, and deploying ML models at scale (PhD in relevant field counts for up to 2 years)
• Demonstrated track record delivering high-impact machine learning models in low-latency production environments
• Strong Python proficiency; ability to write production-quality code
• Experience building and evaluating models for tabular classification problems (gradient-boosted decision trees such as LightGBM, XGBoost, or CatBoost preferred)
• Hands-on experience with a deep learning framework (PyTorch preferred)
• Experience with distributed data processing or parallel compute frameworks (Spark preferred; Ray, Dask, or equivalent acceptable)
• 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) as part of routine development workflows

Benefits

• Remote position based in Canada