Machine Learning Engineer
Build and deploy large-scale ML models for real-time fraud detection, engineering features from 1T+ events and maintaining production MLOps infrastructure on GCP. Requires 4+ years experience with Java/Scala, Python, Spark/Flink, and distributed systems.
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 automated model training and deployment infrastructure, ensuring frictionless CI/CD of newly trained models
System Optimization
- Write high-performance code to minimize scoring latency at runtime, ensuring 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 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
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