Machine Learning Engineer II (Fraud) Remote Canada | $175K Salary

📍 Location: ca

🏷 Type: Not specified

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Job Overview

Affirm is hiring a Mid-Level Machine Learning Engineer focused on fraud detection systems within a high-scale fintech environment. This role centers on building real-time decisioning models that balance fraud prevention, customer experience, and transaction conversion. Ideal candidates bring 2+ years of ML engineering experience, strong Python development skills, and hands-on experience with tabular modeling and production ML systems. You will collaborate cross-functionally to deploy robust models that evolve alongside rapidly changing fraud patterns.

🌍 Work Eligibility & Location

This is a fully Remote role within Canada, meaning international applicants without Canadian work authorization may face limitations. Since visa sponsorship is not clearly offered, candidates should assume eligibility requirements are strict. The remote-first structure allows flexibility, but employment is geographically constrained to maintain compliance with payroll and legal frameworks.

💰 Salary Intelligence

The compensation falls within a highly competitive mid-level ML engineering band in Canada, particularly within fintech. The $125K–$175K base is complemented by equity and stipends, making total compensation attractive relative to market benchmarks. For a mid-level role, this indicates strong demand for fraud-focused ML expertise and production experience.

📊 Role Breakdown

This role blends model development (40%), data pipeline engineering (25%), production deployment (20%), and monitoring/iteration (15%). You will design and optimize fraud prediction models using gradient-boosted decision trees such as LightGBM, XGBoost, or CatBoost, alongside deep learning frameworks like PyTorch where appropriate. A key focus is building feature pipelines that integrate both proprietary and third-party signals.

You will also prototype new modeling strategies, run offline experiments, and transition successful approaches into real-time or batch production systems. Continuous improvement is critical: you will monitor model drift, evaluate data health, and implement retraining workflows. Collaboration across engineering, product, and analytics ensures alignment between technical performance and business impact.

🧩 Required Skills & Fit

📈 Difficulty & Competitiveness

Despite being labeled mid-level, the hiring bar is relatively high due to the production-critical nature of fraud systems. Candidates must demonstrate 2+ years of hands-on experience in real-world ML deployment, not just experimentation. The combination of data engineering, modeling, and system reliability makes this role competitive, especially within a global fintech hiring market.

🚀 Career Impact

This role delivers strong career leverage by combining fintech exposure with real-time machine learning systems. Experience here significantly boosts your profile for senior ML engineering roles, particularly in fraud, risk, or payments. The focus on end-to-end ML lifecycle ownership positions you for high-impact technical leadership paths.

📋 Key Responsibilities

You will develop and optimize fraud detection models using Python and frameworks like LightGBM and PyTorch. A major responsibility is to build scalable feature pipelines leveraging Spark or similar distributed systems. You will prototype new features and modeling approaches, conduct offline experimentation, and deploy models into production systems with strict latency and reliability constraints.

Additionally, you will monitor model performance, track data drift, and maintain retraining pipelines. Cross-functional collaboration is essential as you translate business requirements into ML solutions and communicate results to both technical and non-technical stakeholders.

🎯 Application Strategy

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🧠 Fit & Positioning Analysis

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📅 Application Signals

This role shows moderate urgency given ongoing hiring for fraud and risk infrastructure. However, competition is high due to remote flexibility within Canada and strong compensation. Candidates with direct experience in production ML systems and fraud or fintech domains will have a clear advantage in early screening stages.

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✅ Job Source & Verification

This job listing is sourced from Affirm’s official careers page, ensuring high credibility and accuracy. Details including salary, responsibilities, and qualifications are directly aligned with the employer’s posting. Last updated: April 2026, making this information current and reliable for active job seekers.