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Posted 1 weeks agoMachine Learning Engineer
at Sift
United StatesRemote
Requirements
- THE ROLE: As a Machine Learning Engineer at Sift, you will bridge the gap between data science and large-scale distributed systems.
- 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.
- - 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 databases.
- experience building and deploying large-scale machine learning models into high-traffic production environments. - Solid Programming Foundations: Strong proficiency in Java or Scala (for our production backend) as well as Python (for data analysis and model prototyping). - Distributed Systems & Big Data: Practical
- experience with Databricks and big data processing frameworks like Apache Spark, Apache Flink, or Hadoop, and working with NoSQL data stores like Bigtable.
- - Strong Mathematical Foundations: Deep understanding of statistical modeling, probability, and standard machine learning algorithms (e.g., XGBoost, Random Forests, Neural Networks, and Clustering techniques).
- - System Design Mentality: Ability to reason through data consistency, pipeline failures, and performance constraints in a distributed, multi-tenant cloud environment (GCP).
Experience
- Experience: 4+ years of professional
Benefits
- BONUS POINTS (PREFERRED QUALIFICATIONS): -
Additional details
- 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.
- - 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.
- - Collaborative Innovation: Work cross-functionally with Core Infrastructure, Product Management, and Data Science teams to translate business-level fraud patterns into robust algorithmic solutions.