Machine Learning Engineer – Fraud Detection (Remote, US)

About the Role

CareerPath is hiring a Machine Learning Engineer to build reliable, scalable systems for fraud detection. You’ll work across machine learning, data pipelines, and Go backend services to deliver real-time predictions and own solutions from raw data through production monitoring.

Compensation and Work Arrangement

• Base salary: $175,000 per year, as listed in the job posting
• Equity: Competitive equity
• Employment: Full-time, remote-first; candidates must be based in the US or Canada
• Hiring: 3 openings
• Office locations: Bay Area, New York City, Austin, Toronto, and São Paulo
• Listed locations: New York, San Francisco, South Bay Area, and Los Angeles

What You’ll Do

• Build and optimize data pipelines and backend services that process device and behavioral data in real time.
• Develop and deploy fraud detection machine learning models, ensuring production reliability and efficiency.
• Transform raw data into production-ready features for fraud detection systems.
• Partner with platform and backend engineers to integrate models into production systems.
• Maintain strong security, privacy, and compliance practices.
• Champion testing, documentation, and observability best practices.

Required Qualifications

• 5–8 years of software engineering experience, including strong backend development in Go or Python and machine learning experience.
• End-to-end ownership of machine learning models, including feature pipelines, deployment, monitoring, and iteration—not experimentation alone.
• Professional fraud domain experience, such as bot detection, device fingerprinting, or VPN and proxy detection.
• Experience building latency-sensitive machine learning systems that serve real-time predictions at scale.
• Familiarity with machine learning platform tooling, including feature pipelines and model drift monitoring.

Visa Sponsorship

H-1B sponsorship is not available. TN visas are acceptable; L-1 and O-1 cases are considered individually.

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