Affirm
Senior Machine Learning Engineer (Fraud)
Remote Canada · Senior
Sponsorship not specified$153k-$213kDetected 22 hours ago
PythonRESTMachine LearningDeep LearningPyTorchSparkAirflowA/B TestingCommunicationCollaboration
About the role
- Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest.
- Pay Grade - N Equity Grade - 6 Employees new to Affirm typically come in at the start of the pay range.
Responsibilities
- You will lead development of new fraud prediction models using a mix of approaches for tabular, graph, and behavioral data
- You will build and scale feature pipelines and training datasets from proprietary and third-party signals, partnering with data and platform teams when needed.
- You will prototype new modeling ideas and features, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls.
- Identify and implement foundational improvements to how the team builds models.
- You will 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.
- Experience building and evaluating models for tabular classification problems (preferably gradient-boosted decision trees like LightGBM/XGBoost/CatBoost, or similar).
Requirements
- You have 6+ years experience researching, training, tuning and launching ML models at scale.
- Relevant PhD can count for up to 2 years of experience.
- Experience with ML lifecycle tooling for training orchestration, experimentation, and model monitoring (e.g., Kubeflow, Airflow, MLflow, or equivalent internal platforms).
- What we look for - You have 6+ years experience researching, training, tuning and launching ML models at scale.
- You have 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.
- You are comfortable navigating a large code base, debugging others' code, and providing feedback to other engineers through code reviews.
Nice to have
- Experience working with distributed data processing or parallel compute frameworks (Spark preferred
- Ray/Dask or similar).
Compensation
- Employees new to Affirm typically come in at the start of the pay range.
- CAN base pay range per year: $153,000 - $213,000
Benefits
- You will instrument and monitor model and data health, and help define retraining/backtesting workflows as fraud patterns evolve.
- Track record of delivering high impact machine learning models in a low latency live setting
- Experience with a deep learning framework (PyTorch preferred).
Company info
- Proficient in using AI-powered developer tools (e.g., Claude Code, Cursor, or similar) to accelerate iteration, debugging, and code quality as part of day-to-day development workflows.
This listing is sourced directly from Affirm's careers page and normalized into a canonical job model.