Machine Learning Engineer II - Fraud
New
A
AffirmFinTech
Remote CanadaFull-TimeMiddle
Salary125000 - 175000 CAD per year
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Job Details
- Experience
- 2+ years
- Required Skills
- PythonKubeflowMLFlowPyTorchAirflowSpark
Requirements
- 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 (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)
- Proficient in using AI-powered developer tools (e.g., Claude Code, Cursor, or similar)
- Mastered taking a simple problem into a solution that interacts with multiple software components, by writing clear, well tested and extensible code
- Comfortable navigating a large code base, debugging others' code, and providing feedback through code reviews
- Strong verbal and written communication skills
Responsibilities
- Develop and iterate on fraud prediction models using a mix of approaches for tabular 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 best-performing approaches into production with appropriate risk controls
- Help 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
- 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
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