Machine Learning Engineer

Join Sift as a Machine Learning Engineer, where you will bridge the gap between data science and large-scale distributed systems. You will build end-to-end pipelines, design and deploy online machine learning models, engineer high-frequency time-series features, maintain and enhance our automated model training and deployment infrastructure, and optimize system performance. You will collaborate with cross-functional teams to translate business-level fraud patterns into robust algorithmic solutions.

• Concevoir, construire et déployer des modèles d'apprentissage automatique en ligne pour détecter les vecteurs de fraude en temps réel.
• Ingénierie des fonctionnalités à grande échelle, en optimisant l'extraction des signaux et la reconnaissance des motifs.
• Maintenir et améliorer notre infrastructure d'entraînement et de déploiement de modèles automatisée, en garantissant une intégration continue et un déploiement continu.
• System Design Mentality: Ability to reason through data consistency, pipeline failures, and performance constraints in a distributed, multi-tenant cloud environment (GCP)
• Experience: 4+ years of professional experience building and deploying large-scale machine learning models into high-traffic production environments
• 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)
• Solid Programming Foundations: Strong proficiency in Java or Scala (for our production backend) as well as Python (for data analysis and model prototyping)
• 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