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Senior Machine Learning Engineer (Fraud)

Affirm· Checkout
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Hybrid (Remote Canada)CA$153k - CA$213kfull-timePosted 3mo ago

About the role

Senior ML Engineer on the Fraud team at Affirm, focusing on real-time transaction decisions, building and productionizing ML models for fraud detection, and collaborating cross-functionally to balance fraud loss with customer experience and conversion.

Responsibilities

  • Lead development of new fraud prediction models using a mix of approaches for tabular, graph, and behavioral data
  • Build and scale feature pipelines and training datasets from proprietary and third-party signals, partnering with data and platform teams
  • Prototype new modeling ideas and features, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls
  • Productionize models: integrate into batch and/or real-time decision systems, and improve reliability, latency, and operational robustness
  • Instrument and monitor model and data health, and help define retraining/backtesting workflows as fraud patterns evolve
  • Identify and implement foundational improvements to how the team builds models
  • 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

Qualifications

  • 6+ years experience researching, training, tuning and launching ML models at scale (PhD can count for up to 2 years)
  • Track record of delivering high impact machine learning models in a low latency live setting
  • Strong Python skills and experience writing production-quality code
  • Experience building and evaluating models for tabular classification problems (gradient-boosted trees like LightGBM/XGBoost/CatBoost or similar)
  • Experience with a deep learning framework (PyTorch preferred)
  • Experience 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)
  • Proficient in using AI-powered developer tools to accelerate iteration, debugging, and code quality
  • Ability to take simple problems to multi-component solutions with clear, well-tested, extensible code
  • Comfort navigating large codebases, debugging, and providing code reviews
  • Strong communication skills for collaboration with a global engineering team
  • Demonstrated ownership of growth and proactive feedback seeking

Skills

Required:PythonTabular data modelingGradient-boosted treesPyTorchDistributed data processingML lifecycle toolingCode reviewsCommunication

Benefits

  • Health coverage at no cost for employees and their dependents
  • Monthly stipends for tech setup and health/wellness options
  • Flexible time off and generous holiday calendars
  • Employee stock purchase plan (ESPP)
  • Remote-first with flexibility; in-person onboarding; occasional office requirements
Last updated Sep 16, 2026. Interested? Apply on the company site.

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