Senior Machine Learning Engineer (Fraud)

Role overview Build and operate machine learning systems that assess fraud risk in real time, helping protect customers and merchants while balancing losses, conversion, and the customer experience. The work spans model research, data and feature pipelines, production integration, and ongoing monitoring as fraud behavior changes.

Responsibilities - Develop fraud prediction models using tabular, graph, and behavioral data. - Build feature pipelines and training datasets from internal and external signals, coordinating with data and platform partners. - Prototype modeling approaches, run offline evaluations, and move effective solutions into production with suitable risk controls. - Integrate models into batch or low-latency decision systems, improving reliability, response time, and operational resilience. - Monitor model and data quality and help establish backtesting and retraining processes. - Improve shared modeling practices and work across engineering, analytics, product, and platform teams to set requirements and explain tradeoffs.

Requirements - At least six years of experience researching, training, tuning, and launching machine learning models at scale; a relevant PhD may count for up to two years. - Demonstrated success delivering impactful models in low-latency production environments. - Strong Python skills and experience writing production-quality software. - Experience building and evaluating tabular classification models; gradient-boosted tree methods are particularly relevant. - Experience with a deep learning framework and distributed data processing or parallel computing. PyTorch and Spark are examples named in the source. - Familiarity with tools for training orchestration, experimentation, and model monitoring, such as MLflow or equivalent systems. - Ability to navigate large codebases, debug existing systems, review code, and deliver clear, tested, extensible solutions. - Strong written and verbal communication and a record of seeking feedback and taking ownership of professional growth.

Nice to have - Experience with graph or behavioral modeling, and familiarity with AI-assisted developer tools as part of everyday engineering work.

Benefits and work setup - Remote role for candidates residing in Alberta, British Columbia, Manitoba, New Brunswick, Newfoundland and Labrador, Nova Scotia, Ontario, Prince Edward Island, or Saskatchewan. - The listing describes health, dental, and vision coverage, monthly technology and wellness stipends, flexible time off, and an employee stock purchase plan.