Real-Time ML Engineer for Fraud Detection & Scalable Infra

sift.com; San Francisco (CA); Sur place; USD 180 000 - 240 000; Plein temps

Résumé du poste

Sift is hiring a Machine Learning Engineer in San Francisco to bridge data science and large-scale distributed systems. You will design end-to-end pipelines that extract signals, train merchant-specific models, and serve predictions with low latency in production.

You will work on an automated ML ecosystem that adapts models using streaming telemetry, collaborate with infra, product, and data science teams, and help defeat evolving fraud.

Qualifications

• 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).
• 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).
• Ability to reason through data consistency, pipeline failures, and performance constraints in a distributed, multi-tenant cloud environment (GCP).

Responsabilités

• 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.
• Maintain and enhance our automated model training and deployment infrastructure, ensuring CI/CD of newly trained models.
• Write high-performance code to minimize scoring latency at runtime, ensuring scalable core ML services across distributed databases.
• Collaborate cross-functionally with Core Infrastructure, Product Management, and Data Science teams to translate fraud patterns into robust algorithms.

Connaissances

ML in production

Java/Scala

Python

Distributed systems

Statistical modeling

Outils

Databricks

Apache Spark

Apache Flink

Hadoop

Bigtable

Docker

Kubernetes

Description du poste

Sift is hiring a Machine Learning Engineer in San Francisco to bridge data science and large-scale distributed systems. You will design end-to-end pipelines that extract signals, train merchant-specific models, and serve predictions with low latency in production.

You will work on an automated ML ecosystem that adapts models using streaming telemetry, collaborate with infra, product, and data science teams, and help defeat evolving fraud.