Senior Machine Learning Engineer - Fraud ML
New
A
AffirmFinTech
Remote CanadaFull-TimeSenior
Salary150000 - 200000 CAD per year
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Job Details
- Experience
- 6+ years
- Required Skills
- PythonKubeflowMLFlowPyTorchAirflowSpark
Requirements
- 6+ years experience researching, training, tuning and launching ML models at scale (Relevant PhD can count for up to 2 years of experience)
- 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 (preferably gradient-boosted decision trees like 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 internal platforms)
- Proficient in using AI-powered developer tools (e.g., Claude Code, Cursor, or similar) to accelerate iteration, debugging, and code quality
- Mastered taking a simple problem or business scenario into a solution that interacts with multiple software components, and executing on it by writing clear, easily understood, well tested and extensible code
- Comfortable navigating a large code base, debugging others' code, and providing feedback to other engineers through code reviews
- Strong verbal and written communication skills that support effective collaboration with global engineering team
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 when needed
- 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
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