Machine Learning Engineer

Job not on LinkedIn

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🏄 California – Remote

🏄 California – Remote

💵 $140k - $190k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 Machine Learning Engineer

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Sift

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2011

🔒 Cybersecurity

💳 Fintech

☁️ SaaS

Cybersecurity • Fintech • SaaS

Sift is an industry leader in digital fraud management, offering robust AI-powered solutions to combat various forms of online fraud such as account takeovers, payment fraud, and chargebacks. With a scalable platform, Sift enables businesses to secure their operations through advanced decision-making and fraud detection technologies. Their platform is trusted by over 700 global brands, providing precise, user-level insights to turn fraud challenges into opportunities for scalable growth. Sift emphasizes the importance of partnerships and a strong community to enhance their fraud decisioning service, fostering secure business development across various sectors like e-commerce, fintech, and travel. Additionally, Sift is recognized for its insights and efforts in fraud industry leadership, featuring capabilities such as payment protection, account defense, and dispute management.

📋 Description

• 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