Affirm is hiring a remote Senior ML Engineer Fraud!
Affirm is hiring a Senior ML Engineer for its Fraud team in remote Canada. The role focuses on building and maintaining machine learning systems that support real-time transaction decisions, helping balance fraud loss, customer experience, and conversion. The engineer will take models from early ideas through prototyping and production, while monitoring performance as fraud patterns change. The position involves collaboration with ML engineers, platform partners, fraud analytics, product teams, and other stakeholders. Affirm notes that candidates must reside in one of several eligible Canadian provinces and that new hires are typically placed at the start of the pay range.
Key responsibilities
- Lead development of new fraud prediction models using tabular, graph, and behavioral data approaches.
- Build and scale feature pipelines and training datasets from internal and third-party signals.
- Prototype modeling ideas, run offline experiments, and move strong approaches into production with appropriate risk controls.
- Integrate models into batch or real-time decision systems and improve reliability, latency, and operational robustness.
- Instrument and monitor model and data health, and help define retraining and backtesting workflows.
- Identify foundational improvements to the team’s modeling practices.
- Collaborate across engineering, fraud analytics, product, and ML platform teams to define requirements, assess tradeoffs, and communicate results.
Key requirements
- 6+ years of experience researching, training, tuning, and launching ML models at scale; a relevant PhD may count for up to 2 years.
- Proven record of delivering high-impact ML models in low-latency live environments.
- Strong Python skills and experience writing production-quality code.
- Experience building and evaluating tabular classification models, preferably with gradient-boosted decision trees such as LightGBM, XGBoost, or CatBoost.
- Experience with a deep learning framework, with PyTorch preferred.
- Experience with distributed data processing or parallel compute frameworks such as Spark, Ray, or Dask.
- Experience with ML lifecycle tooling for training orchestration, experimentation, and monitoring, such as Kubeflow, Airflow, or MLflow.
- Comfort using AI-powered developer tools to support iteration, debugging, and code quality.
- Ability to navigate a large codebase, debug others’ code, and provide useful code review feedback.
- Strong communication and collaboration skills for working with a global engineering team.
- Seniority: Senior
- Work mode: Remote in Canada; candidates must reside in Alberta, British Columbia, Manitoba, New Brunswick, Newfoundland and Labrador, Nova Scotia, Ontario, Prince Edward Island, or Saskatchewan
- Salary: CAD $153,000 – $213,000 per year
- Key skills: Machine learning, Fraud detection, Python, Tabular classification, Gradient-boosted decision trees, LightGBM, XGBoost, CatBoost, PyTorch, Spark, Ray, Dask, Feature pipelines, Real-time decision systems, Model monitoring
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