Sift sells an ML-driven fraud and abuse detection platform built on a heavy streaming and big-data stack: Kafka, Flink, Spark, Databricks and Bigtable across AWS and GCP, with XGBoost for modeling. Hiring is slow but senior-weighted, split across engineering, security and legal, pointing to work on ML training infrastructure, secure SDLC tooling and contract automation rather than broad headcount growth.
Notable leadership hires: Sales Director
Sift is a privately held company founded in 2011 and headquartered in San Francisco, California. It provides a SaaS platform that uses machine learning and predictive analytics to detect payment fraud, online abuse and other digital trust and safety risks for consumer-facing brands. The company employs 201–500 people and currently hires in the United States. Its engineering stack spans Python, Go, Java, Scala and Ruby on Rails, running on Kubernetes with PostgreSQL, Redis and gRPC.
Sift runs on AWS and GCP with Python, Go, Java, Scala and Ruby on Rails, and processes data with Kafka, Apache Flink, Apache Spark, Databricks, Bigtable and Airflow. XGBoost supports its ML models.
Yes, engineering is Sift's largest hiring area, alongside security, legal and sales roles. Openings skew senior and are based in the United States, though overall hiring velocity is currently minimal.
Sift is headquartered in San Francisco, California, and has 201–500 employees. It was founded in 2011 and is privately held.
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Sift's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.
