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Posted 5 days agoSenior Machine Learning Engineer (Fraud)
at Affirm
CanadaRemote
Responsibilities
- - Identify and implement foundational improvements to how the team builds models.
- - Track record of delivering high impact machine learning models in a low latency live setting
Requirements
- On the ML Fraud team, you’ll build and improve machine learning systems that make real-time transaction decisions, protecting consumers and merchants while balancing fraud loss, customer experience, and conversion.
- You’ll work closely with experienced ML engineers, platform partners, and cross-functional stakeholders to take models from idea to prototype to production, and to keep them healthy with strong measurement and monitoring as fraud patterns evolve. What you’ll do
- - 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. What we look for - 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.
- - Strong Python skills and
- experience writing production-quality code. -
- 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 as part of day-to-day development workflows.
- - 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.
- Affirmers in proximal roles have the flexibility to work remotely, but will occasionally be required to work out of their assigned Affirm office.
Benefits
- - You have strong verbal and written communication skills that support effective collaboration with our global engineering team. Pay Grade - N Equity Grade - 6
- Employees new to Affirm typically come in at the start of the pay range. Affirm focuses on providing a simple and transparent pay structure which is based on a variety of factors, including location,
- Base pay is part of a total compensation package that may include monthly stipends for health, wellness and tech spending, and
- benefits (including 100% subsidized medical coverage, dental and vision for you and your dependents).
- In addition, the employees may be eligible for equity rewards offered by Affirm Holdings, Inc. (parent company).
- CAN base pay range per year: $153,000 - $213,000
- benefits that are anchored to our core value of people come first. Some key highlights of our benefits package include:
- Health care coverage - Affirm covers all premiums for all levels of coverage for you and your dependents
- Flexible Spending Wallets - generous stipends for spending on Technology, Food, various Lifestyle needs, and family forming expenses
- Time off - competitive vacation and holiday schedules allowing you to take time off to rest and recharge
- ESPP - An employee stock purchase plan enabling you to buy shares of Affirm at a discount
Additional details
- 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.
- - 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.
- - You productionize models: integrate into batch and/or real-time decision systems, and improve reliability, latency, and operational robustness.
- - You will instrument and monitor model and data health, and help define retraining/backtesting workflows as fraud patterns evolve.
- Experience building and evaluating models for tabular classification problems (preferably gradient-boosted decision trees like LightGBM/XGBoost/CatBoost, or similar). -
- - You are comfortable navigating a large code base, debugging others' code, and providing feedback to other engineers through code reviews. - Your
- experience demonstrates that you take ownership of your growth, proactively seeking feedback from your team, your manager, and your stakeholders.
- This remote role is open only to candidates residing in Alberta, British Columbia, Manitoba, New Brunswick, Newfoundland and Labrador, Nova Scotia, Ontario, Prince Edward Island, or Saskatchewan. #LI-Remote