Machine Learning Engineer
Build and deploy large-scale online ML models and end-to-end pipelines for real-time fraud detection at Sift, working on automated training systems that process over 1T events. Requires 4+ years production ML experience, strong Python/Java/Scala skills, and distributed systems expertise with Spark, Databricks, and GCP.
About the job
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 databases.
Collaborative Innovation: Work cross-functionally with Core Infrastructure, Product Management, and Data Science teams to translate business-level fraud patterns into robust algorithmic solutions.
Requirements
- 4+ years of professional experience building and deploying large-scale machine learning models into high-traffic production environments.
- Strong proficiency in Java or Scala (for our production backend) as well as Python (for data analysis and model prototyping).
- Practical experience with Databricks and big data processing frameworks like Apache Spark, Apache Flink, or Hadoop, and working with NoSQL data stores like Bigtable.
- Deep understanding of statistical modeling, probability, and standard machine learning algorithms (e.g., XGBoost, Random Forests, Neural Networks, and Clustering techniques).
- Ability to reason through data consistency, pipeline failures, and performance constraints in a distributed, multi-tenant cloud environment (GCP).
Preferred Qualifications
- Experience explicitly in the fraud detection, risk mitigation, or cyber-security domains.
- Deep knowledge of streaming architectures (e.g., Apache Kafka).
- Familiarity with containerization and orchestration tools like Docker and Kubernetes.
- Familiarity with leveraging AI coding assistants (e.g., Claude Code) to accelerate development and model prototyping.
Skills
Machine Learning, Python, Java, Scala, Databricks, Spark, Apache Flink, Hadoop, Bigtable, Xgboost, Random Forests, Neural Networks, GCP, Apache Kafka, Docker
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