Senior Machine Learning Engineer Fraud - jobgether

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Machine Learning Engineer (Fraud) based in Canada.

This role offers the opportunity to build advanced machine learning systems that protect digital transactions and improve customer trust. You will design, develop, and deploy fraud detection models that operate at scale in real-time environments. The position combines applied machine learning, engineering excellence, and close collaboration with product and analytics teams. You will work on complex fraud challenges by transforming data signals into reliable, impactful solutions. The role provides ownership over the full ML lifecycle, from experimentation and modeling to production deployment and monitoring. You will contribute to improving model performance, platform capabilities, and engineering practices in a fast-moving environment. This opportunity is ideal for an experienced ML engineer passionate about solving high-impact problems through data and technology.

Accountabilities:

The Senior Machine Learning Engineer will design, build, and scale fraud detection solutions that balance security, user experience, and business performance. This role requires strong technical expertise, ownership of machine learning systems, and the ability to collaborate across engineering, analytics, product, and platform teams.

• Develop and improve machine learning models for fraud prediction using tabular, graph, and behavioral data.
• Build and maintain scalable feature pipelines and training datasets using internal and external data sources.
• Prototype new modeling approaches, conduct offline experiments, and transition successful solutions into production.
• Integrate machine learning models into batch and real-time decision systems while improving reliability, latency, and scalability.
• Monitor model performance, data quality, and system health to ensure continued effectiveness as fraud patterns evolve.
• Define and improve model retraining, backtesting, and monitoring workflows.
• Identify and implement foundational improvements to machine learning development processes and infrastructure.
• Partner with engineering, fraud analytics, product, and ML platform teams to define requirements and evaluate technical trade-offs.
• Communicate technical findings, model performance, and recommendations clearly to both technical and non-technical stakeholders.
• Contribute to code quality through testing, documentation, debugging, and peer code reviews.
• Navigate complex codebases and provide technical guidance to other engineers.
• Take ownership of personal and team growth by actively seeking feedback and continuously improving engineering practices.

Requirements: