Senior Software Engineer, Trust and Safety Engineering

Box Box · Enterprise · Redwood City, CA · Security

Senior Software Engineer on the Trust and Safety Engineering team at Box. This role focuses on protecting the platform and customers from abuse, fraud, and malicious actors by designing and building security systems, scalable APIs, backend services, and behavioral detection systems. It involves applying machine learning to model malicious behavior and strengthening platform defenses with mitigation controls. The role requires experience with containerized microservices and distributed environments, with a preference for ML models and trust and safety systems.

What you'd actually do

  1. Design, build, and continuously improve security systems that protect Box, its platform, and its customers from abuse, fraud, malicious actors, and insider threats.
  2. Develop scalable APIs and backend services that detect, prevent, and disrupt abuse campaigns across Box’s microservice infrastructure.
  3. Build behavioral detection systems and near-real-time mitigation workflows to identify suspicious usage patterns, risky traffic, and emerging attack techniques.
  4. Apply analytics and machine learning approaches to model malicious behavior, improve detection accuracy, and reduce false positives.
  5. Strengthen platform defenses through rate limiting, automated enforcement, and other mitigation controls that balance security with customer experience.

Skills

Required

  • 6+ years of professional software development experience in one or more of the following languages: Java, Python, PHP, or Scala
  • Experience building containerized microservices in large-scale, distributed environments
  • Strong analytical, system design, engineering, planning, and problem-solving skills

Nice to have

  • experience with machine learning models
  • large-scale datasets
  • Java Spring/Spring Boot
  • Terraform-based public cloud infrastructure
  • trust and safety, abuse, or fraud mitigation systems

What the JD emphasized

  • machine learning models
  • trust and safety
  • abuse
  • fraud mitigation systems

Other signals

  • building behavioral detection systems
  • machine learning models to identify suspicious activity
  • intelligent, scalable systems