Media quality and ad fraud detection platform for digital advertising
Integral Ad Science operates a Java-based media measurement platform spanning ad verification, viewability, and fraud detection across YouTube, Meta, TikTok, and Pinterest. The stack—Java microservices, Spark, Kafka, Kinesis, Databricks—reflects heavy data-pipeline engineering, now adopting CI/CD practices. Active hiring across engineering (16 open roles) and a multi-project roadmap targeting ML model testing and multimedia classification suggest a shift from rule-based fraud detection toward predictive systems.
Integral Ad Science is a media quality and measurement platform serving advertisers, publishers, and ad platforms globally. Founded in 2009 and headquartered in New York, the company provides ad verification, viewability measurement, fraud detection, and optimization tooling designed to ensure ads reach real audiences in safe environments. The technical footprint spans cloud infrastructure (AWS, GCP, Azure), streaming (Kafka, Kinesis), analytics (Spark, Databricks, Delta Lake), and first-party integrations with major platforms (YouTube, Meta, TikTok, Pinterest). Revenue and scaling operations are supported by a sales org and onsite support teams, with product and data functions embedded across a primarily engineering-heavy structure.
Java, Spring Framework, Spring Boot, AWS/GCP/Azure, Kafka, Spark, Databricks, Delta Lake, React, Python, DynamoDB, Lambda, and integrations with YouTube, Meta, TikTok, Pinterest.
India, United States, Singapore, Japan, Ireland, United Kingdom, Indonesia, Australia, Germany, Italy, France, and Taiwan.
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