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Machine Learning Engineer (Credit Risk & Decisioning) - Remote Canada
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AI Summary
Develop and enhance machine learning systems for real-time transaction decisions to protect consumers and merchants. Lead the development of new fraud prediction models, build feature pipelines, and productionize models. Requires 6+ years of ML model experience, strong Python, and experience with ML lifecycle tooling.
Key Highlights
Develop and enhance ML systems for real-time transaction decisions.
Lead model development, feature engineering, and productionization.
Requires 6+ years of ML experience, Python, and ML lifecycle tooling.
Key Responsibilities
Lead development of new fraud prediction models
Build and scale feature pipelines and training datasets
Prototype new modeling ideas and features
Productionize models
Instrument and monitor model and data health
Identify and implement foundational improvements to model building
Collaborate across teams to define requirements and communicate results
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Technical Skills Required
Benefits & Perks
Health coverage
Spending stipends
Time off
Employee stock purchase plan
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Nice to Have
Relevant PhD
Gradient-boosted decision trees (LightGBM/XGBoost/CatBoost)
Spark
Kubeflow, Airflow, MLflow
AI-powered developer tools
Job Description
At Affirm, we exist for the moments that matter—giving people a clear, predictable way to pay over time, with no hidden fees, no surprises, and no tradeoffs on what matters most.
On the ML Fraud team, you’ll build and improve machine learning systems that make real-time transaction decisions, protecting consumers and merchants while balancing fraud loss, customer experience, and conversion. You’ll work closely with experienced ML engineers, platform partners, and cross-functional stakeholders to take models from idea to prototype to production, and to keep them healthy with strong measurement and monitoring as fraud patterns evolve.
What you’ll do
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