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Posted:
June 01, 2026
Location:
calgary, ab, Canada
Job Description
Become a Lead Machine Learning Engineer with Affirm's Fraud ML team, specializing in real-time fraud prevention systems. This remote position in Canada offers impactful challenges.
As part of Affirm, you'll leverage your 6+ years of experience to construct and optimize machine learning models focused on fraud detection. Collaboratively engage with various teams to convert ideas into prototypes and production-ready solutions while improving operational efficiencies. Your leadership will help define best practices in model development amid evolving fraud patterns.
Key Responsibilities:
• Spearhead the creation of fraud detection models
• Construct and scale feature pipelines using diverse data
• Drive the deployment of high-performing modeling solutions
• Monitor systems for reliability and operational integrity
• Streamline model building processes and workflows
Requirements:
• Minimum 6 years of experience with ML models
• Expertise in Python for writing p...
As part of Affirm, you'll leverage your 6+ years of experience to construct and optimize machine learning models focused on fraud detection. Collaboratively engage with various teams to convert ideas into prototypes and production-ready solutions while improving operational efficiencies. Your leadership will help define best practices in model development amid evolving fraud patterns.
Key Responsibilities:
• Spearhead the creation of fraud detection models
• Construct and scale feature pipelines using diverse data
• Drive the deployment of high-performing modeling solutions
• Monitor systems for reliability and operational integrity
• Streamline model building processes and workflows
Requirements:
• Minimum 6 years of experience with ML models
• Expertise in Python for writing p...
Apply for this Job
Submit your application for the Lead Machine Learning Engineer for Fraud ML position at Affirm.
Apply Now Save for LaterJob Overview
Job Type:
Full-time
Location:
calgary, Canada
Posted:
June 01, 2026
Deadline:
July 11, 2026