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. 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 will instrument and monitor model and data health, and help define retraining/backtesting workflows as fraud patterns evolve. 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.

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. 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 working with distributed data processing or parallel compute frameworks (Spark preferred;

Experience with ML lifecycle tooling for training orchestration, experimentation, and model monitoring (e.g., 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 straightforward 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.

Pay Grade - N Equity Grade - 6 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 Affir Holdings, Inc. (Location - Remote Canada This remote role is open only to candidates residing in Alberta, British Columbia, Manitoba, Recent Brunswick, Newfoundland and Labrador, Nova Scotia, Ontario, Prince Edward Island, or Saskatchewan. Remote‑first with flexibility built in Affir is proud to be a remote‑first company.

Some positions may occasionally require in‑person work at an Affir office, and a few are office‑based due to the nature of the work.

Health coverage at no cost: Flexible time off and generous holiday calendars help you rest when you need to. We’re committed to providing an inclusive interview process, including accommodations for candidates with disabilities. If you need support, we’re happy to help.

📌 Senior Machine Learning Engineer (Fraud) (Winnipeg)
🏢 Affirm
📍 Winnipeg