Machine Learning Engineer
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Job Description
Machine Learning Engineer at Sift developing large-scale models to detect fraud. Responsible for creating automated ML frameworks and overseeing production deployment.
Responsibilities:
- Design, build, and deploy online machine learning models to catch evolving fraud vectors in real time.
- Engineer high-frequency time-series features from over 1 trillion behavioral events, optimizing for low-latency signal extraction and pattern recognition.
- Maintain and enhance our automated model training and deployment infrastructure, ensuring frictionless CI/CD of newly trained models.
- Write high-performance code to minimize scoring latency at runtime, ensuring our core ML services scale seamlessly across distributed databases.
- 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 as well as Python.
- 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).
Benefits:
- Offers Equity
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