Machine Learning Engineer - Remote (US) at CareerPath

About the Role Join CareerPath as a Machine Learning Engineer and design cutting-edge systems that enable real-time fraud detection. You will work across modeling, data pipelines, and backend services (Go) to ensure ML models operate reliably, efficiently, and at scale. This role offers a unique opportunity to blend applied machine learning with large-scale systems engineering, owning end-to-end solutions that address complex, high-stakes challenges. Compensation & Work Arrangement Salary: $175,000 – $220,000 per year Equity: Competitive equity package Visa Sponsorship: Not available (TN: OK, L1/O1: Case-by-case, No H1-B) Work Type: Full-time, remote-first (US or Canada-based) Office Locations: Bay Area, NYC, Austin, Toronto, São Paulo Hiring Count: 3 openings Key Responsibilities Build and optimize data pipelines and backend services to process device and behavioral data in real time Develop and deploy machine learning models for fraud detection, ensuring production reliability and efficiency Transform raw data into production-ready features feeding fraud detection systems Collaborate with platform and backend engineers to seamlessly integrate ML models Maintain high standards of security, privacy, and compliance Champion best practices in testing, documentation, and observability Mandatory Requirements Experience: 5–8 years in software engineering with strong backend skills (Go or Python) and machine learning expertise ML Ownership: Proven end-to-end ML model ownership including feature pipelines, deployment, monitoring, and iteration (beyond experimentation) Fraud Domain: Experience with bot detection, device fingerprinting, VPN/proxy detection, or related fraud areas Technical Skills: Built latency-sensitive ML systems serving real-time predictions at scale; familiarity with ML platform tooling such as feature pipelines and drift detection