Senior Machine Learning Engineer (Fraud)
$153,000–$213,000 year
RemoteOntario, California, United States or New Brunswick, New Jersey, United States
Job Summary
Lead development of fraud prediction models using tabular, graph, and behavioral data, while building and scaling feature pipelines from proprietary and third-party signals. Prototype new modeling ideas, run offline experiments, and drive best-performing approaches into production with appropriate risk controls. Productionize models by integrating them into batch and/or real-time decision systems to improve reliability, latency, and operational robustness. Instrument and monitor model and data health, defining retraining and backtesting workflows as fraud patterns evolve. Collaborate across Engineering, Fraud Analytics, Product, and ML Platform to define requirements, evaluate tradeoffs, and communicate results clearly.
Required Qualifications
- 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
- Experience with a deep learning framework
- Experience working with distributed data processing or parallel compute frameworks
- Experience with ML lifecycle tooling for training orchestration, experimentation, and model monitoring
- Proficient in using AI-powered developer tools
- Mastered taking a simple problem or business scenario into a solution that interacts with multiple software components
- Comfortable navigating a large code base, debugging others' code, and providing feedback to other engineers through code reviews
- Experience demonstrates that you take ownership of your growth, proactively seeking feedback from your team, your manager, and your stakeholders
- Strong verbal and written communication skills that support effective collaboration with our global engineering team
- Residing in Alberta, British Columbia, Manitoba, New Brunswick, Newfoundland and Labrador, Nova Scotia, Ontario, Prince Edward Island, or Saskatchewan
Desired Qualifications
- Experience with gradient-boosted decision trees like LightGBM/XGBoost/CatBoost, or similar
- PyTorch preferred
- Spark preferred
- Ray/Dask or similar
- Kubeflow, Airflow, MLflow, or equivalent internal platforms
- Claude Code, Cursor, or similar
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