Machine Learning Engineer @ Sift

Machine Learning Engineer @ Sift - Sift - Back to Sift’s Job Listings Machine Learning Engineer Location San Francisco, California; Remote - USA; Seattle, Washington Employment Type Full time Location Type Hybrid Department Engineering Compensation - Estimated On Target Earnings $140K – $190K • Offers Equity The anticipated starting on-target earnings range for this role is listed above. This role may also be eligible for equity and other benefits. Sift takes into account many factors in determining compensation, including level, location, transferable skills, work experience, business needs, and market demands. Therefore, the pay range / final offer may change or be modified in the future. The Role: As a Machine Learning Engineer at Sift, you will bridge the gap between data science and large-scale distributed systems. You won’t just train models in isolation; you will build end-to-end pipelines that extract signals, train custom models per merchant, and serve predictions at production scale with low latency. You will work on an automated machine learning ecosystem that dynamically recalibrates models based on streaming global telemetry data. What You'll Do: Model Development & Refinement: Design, build, and deploy online machine learning models (including ensemble methods, deep learning, transformer architectures and graph-based models) to catch evolving fraud vectors in real time. Feature Engineering at Scale: Engineer high-frequency time-series features from over 1 trillion behavioral events, optimizing for low-latency signal extraction and pattern recognition. Production MLOps: Maintain and enhance our automated model training and deployment infrastructure, ensuring frictionless continuous integration and continuous deployment (CI/CD) of newly trained models. System Optimization: Write high-performance code to minimize scoring latency at runtime, ensuring our core ML services scale seamlessly across distributed datab