Box Shield is an add-on security control that helps protect the flow of information with precision—enabling classification-based security policies and AI-powered alerts to detect data theft and malicious content. As a Senior Machine Learning Engineer on the Shield team, you will design and deploy enterprise security features that handle complex use-cases at scale. You will build threat detection models for ransomware detection, suspicious session identification, and user behavior analytics; own end-to-end ML pipelines that process high-volume security event streams using Apache Spark, GCP Dataflow, GCP Dataproc, BigQuery, and Vertex AI; create and maintain feature stores to power real-time and batch anomaly detection; deploy, monitor, and iterate ML models in production for enterprise customers. You will collaborate with Platform, Application Engineering, and Product teams to translate security requirements into ML solutions and participate in our on-call rotation to respond to incidents as needed. The role requires 5+ years of applied ML experience, strong Python skills, and hands-on experience with GCP (Vertex AI, BigQuery, Dataflow) or AWS/Azure equivalents. You should be comfortable discussing complex ML concepts with non-technical stakeholders and possess an ownership mindset with a track record of delivering high-quality work. Nice-to-have includes experience in security/threat or fraud detection, streaming/real-time ML, familiarity with FedRAMP/compliance environments, and experience with Java; publications or contributions in ML security are a plus. Languages includ

Box Shield is an add-on security control that helps protect the flow of information with precision—enabling classification-based security policies and AI-powered alerts to detect data theft and malicious content. As a Senior Machine Learning Engineer on the Shield team, you will design and deploy enterprise security features that handle complex use-cases at scale. You will build threat detection models for ransomware detection, suspicious session identification, and user behavior analytics; own end-to-end ML pipelines that process high-volume security event streams using Apache Spark, GCP Dataflow, GCP Dataproc, BigQuery, and Vertex AI; create and maintain feature stores to power real-time and batch anomaly detection; deploy, monitor, and iterate ML models in production for enterprise customers. You will collaborate with Platform, Application Engineering, and Product teams to translate security requirements into ML solutions and participate in our on-call rotation to respond to incidents as needed. The role requires 5+ years of applied ML experience, strong Python skills, and hands-on experience with GCP (Vertex AI, BigQuery, Dataflow) or AWS/Azure equivalents. You should be comfortable discussing complex ML concepts with non-technical stakeholders and possess an ownership mindset with a track record of delivering high-quality work. Nice-to-have includes experience in security/threat or fraud detection, streaming/real-time ML, familiarity with FedRAMP/compliance environments, and experience with Java; publications or contributions in ML security are a plus. Languages includ