Senior Machine Learning Engineer, Shield

WHAT IS BOX?

Box (NYSE:

BOX) is the leader in Intelligent Content Management. Our platform enables organizations to fuel collaboration, manage the entire content lifecycle, secure critical content, and transform business workflows with enterprise AI. We help companies thrive in the new AI-first era of business. Founded in 2005, Box simplifies work for leading global organizations, including JLL, Morgan Stanley, and Nationwide. Box is headquartered in Redwood City, CA, with offices across the United States, Europe, and Asia.

By joining Box, you will have the unique opportunity to continue driving our platform forward. Content powers how we work. It's the billions of files and information flowing across teams, departments, and key business processes every single day: contracts, invoices, employee records, financials, product specs, marketing assets, and more. Our mission is to bring intelligence to the world of content management and empower our customers to completely transform workflows across their organizations.

With the combination of AI and enterprise content, the opportunity has never been greater to transform how the world works together and at Box you will be on the front lines of this massive shift.

Box Shield is an add-on security control that helps you protect the flow of information and reduce content‑centric risks with precision - without slowing down work. It allows classification‑based security controls to automatically prevent data loss, and AI‑powered, context‑aware alerts to detect potential data theft and malicious content. Box Shield enables secure hybrid work from anywhere, anytime, and any device with native tools that help secure content at scale.

The Shield team is looking for ML engineers with a passion for building out enterprise security features that are able to handle complex use‑cases in a robust and easy‑to‑use way. Shield's mission is to protect the flow of an enterprise's information while delivering frictionless user experience so that Box is the tool of choice for secure Cloud Content Management. Shield helps customers keep their content secure by detecting malicious software in their content, potentially compromised accounts, and anomalous behavior so that Administrators have the right information to act before a problem occurs.

As an engineer on our team, you will join a diverse, fast‑paced, mainly backend/core team that works together to build new capabilities that help Box's customers protect their Box content. Security being a horizontal product, you will work across teams to design and implement capabilities that power high‑demand use‑cases in a future‑proof way.

• Build Threat Detection Models:
  Design, train, and deploy ML models for ransomware detection, suspicious session identification, and user behavior analytics, anomaly detection
• Scale Data Pipelines:
  Own end‑to‑end ML pipelines that process high‑volume security event streams using Apache Spark, GCP Dataflow, GCP Dataproc, Big Query and Vertex AI
• Feature Engineering:
  Create and maintain feature stores that power real‑time and batch anomaly detection systems
• Production ML Systems:
  Deploy, monitor, and iterate on ML models in production, serving enterprise customers at scale
• Cross‑functional
  
  Collaboration:
  
  Partner with Platform, Application Engineering, and Product teams to translate security requirements into ML solutions
• Participate in our on‑call rotation, available at all times while on‑call to help respond to and triage any issues that arise.

We are an AI‑first company. This means you approach your work with a growth mindset and find ways to leverage AI to help make faster, smarter decisions that will 10X your impact at Box.

• 5+ years of experience in applied machine learning
• Lead design and implementation efforts in building, deploying and supporting scalable ML systems
• Experience with GCP (Vertex AI, Big Query, Dataflow) or equivalent (AWS Sage Maker, Azure ML)
• Strong communication skills with ability to explain complex ML concepts to non‑technical stakeholders
• Ownership mindset with focus on delivering high‑quality work both technically & collaboratively