Machine Learning Engineer

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.

WHAT WE ARE LOOKING FOR (REQUIREMENTS): - Experience: 4+ years of professional experience building and deploying large-scale machine learning models into high-traffic production environments. - Solid Programming Foundations: Strong proficiency in Java or Scala (for our production backend) as well as Python (for data analysis and model prototyping). - 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). - System Design Mentality: Ability to reason through data consistency, pipeline failures, and performance constraints in a distributed, multi-tenant cloud environment (GCP).

BONUS POINTS (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 Please note: final stage candidates may be asked to travel for in-person final round interviews.

Let's build it together: At Sift, we are intentionally building a diverse, equitable, and inclusive workplace. We believe that diversity drives innovation, equity is a fundamental right, and inclusion is a basic human need. We envision a place where all Sifties feel secure sharing their authentic selves and diverse experiences with their teams, their customers, and their community - ultimately using this empowerment and authenticity to build trust and create a safer Internet.

This document provides transparency around how Sift handles the personal data of job applicants: https://sift.com/recruitment-privacy A little

about us: Sift is the AI-powered fraud platform securing digital trust for leading global businesses. Our deep investments in machine learning and user identity, a data network scoring 1 trillion events per year, and a commitment to long-term customer success empower more than 700 customers to grow fearlessly. Global brands rely on Sift to unlock growth and deliver seamless consumer experiences.

Visit us at sift.com http://sift.com and follow us on LinkedIn https://www.globenewswire.com/Tracker?data=XHeK0v8NcNrEkwcDe8QxwpZeCkdQqNyKlni83U-CUmrprdKXWpVlYOAbVzwe2OmlwIUN-q4HXk4hf_dazpHx2NMM1CW_SYj740q9mxXNQI4=.