Senior Machine Learning Engineer (Fraud)

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:

The ideal candidate is an experienced machine learning engineer with a strong background in developing and deploying ML models at scale. They combine advanced technical skills with strong communication abilities and a passion for building reliable systems that solve complex business problems.

6+ years of experience researching, training, tuning, and launching machine learning models at scale; relevant PhD experience may count toward this requirement.

Proven experience delivering high-impact machine learning models in low-latency production environments.

Strong Python programming skills with experience writing production-quality, maintainable code.

Experience building and evaluating models for tabular classification problems, including approaches such as LightGBM, XGBoost, CatBoost, or similar technologies.

Experience with deep learning frameworks, preferably PyTorch.

Experience working with distributed data processing or parallel computing frameworks such as Spark, Ray, Dask, or equivalent tools.

Experience with ML lifecycle tooling for experimentation, training orchestration, and model monitoring, such as Kubeflow, Airflow, MLflow, or similar platforms.

Ability to use AI-powered developer tools to accelerate development, debugging, experimentation, and code quality.

Strong understanding of designing solutions that integrate across multiple software components.

Ability to work effectively within large codebases and provide constructive engineering feedback.

Strong ownership mindset with a commitment to continuous learning and improvement.

Excellent written and verbal communication skills for collaboration with global technical teams.

Benefits:

Competitive annual base salary range of approximately $153,000 to $213,000 CAD, depending on experience, location, and qualifications.

Remote-first work environment available across eligible Canadian provinces.

Comprehensive health benefits, including fully covered medical, dental, and vision coverage for employees and dependents.

Flexible spending allowances for technology, wellness, lifestyle needs, food, and family-related expenses.

Employee stock purchase plan with discounted share purchase opportunities.

Competitive vacation and holiday programs to support work-life balance.

Opportunities to work on high-impact machine learning projects with real-world business impact.

Supportive and inclusive workplace culture focused on innovation, collaboration, and professional growth.

How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best!  Why Apply Through Jobgether?    Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.     #LI-CL1