Machine Learning Engineer III, Shield

Box•Redwood City, CA

7d•Hybrid

About The Position

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. WHY BOX NEEDS YOU Box Shield is an add-on security layer that protects the flow of information and reduces content-centric risk without adding friction. It uses classification-based controls to prevent data loss and AI-powered, context-aware alerts to detect malware, compromised accounts, and malicious behavior at scale. Shield’s mission is to secure enterprise content while delivering a seamless user experience, ensuring Box remains the platform of choice for secure Intelligent Content Management. Shield keeps customer content safe by surfacing malware, compromised accounts, and anomalous behavior early, giving administrators the context they need to take proactive action before incidents escalate. The Shield team is hiring Machine Learning engineers to build and scale ML systems that safeguard millions of users and billions of files from ransomware, data exfiltration, insider threats, and emerging AI-based attacks. As part of a fast-paced backend/core team, you’ll collaborate across Box to ship future-proof security capabilities for high-demand use cases.

Requirements

3+ years of experience in applied machine learning
Strong programming skills in Python
Deployed and maintained ML models serving real traffic
Experience with GCP (Vertex AI, BigQuery, Dataflow) or equivalent (AWS SageMaker, Azure ML)
Deep understanding of feature engineering, model evaluation, and MLOps
Strong communication skills with ability to explain complex ML concepts to non-technical stakeholders

Nice To Haves

Experience in security/threat detection, anomaly detection, or fraud detection
Experience with sequential data, anomaly detection, or behavioral modeling (time-series forecasting, LSTM, Transformers, or similar)
Knowledge of LLMs and AI safety (prompt injection, guardrails, red-teaming)
Experience with streaming/real-time ML systems
Familiarity with Java for service integrations
Publications or contributions in ML security

Responsibilities

Build Threat Detection Models: Design, train, and deploy ML models for ransomware detection, suspicious session identification, and user behavior analytics
Scale Data Pipelines: Own end-to-end ML pipelines that process high-volume security event streams using Apache Spark, GCP Dataflow, 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.

Benefits

Box lives its values, with community and in-person collaboration being a core part of our culture.
Boxers are expected to work from their assigned office a minimum of 3 days per week.
This role is also eligible for equity and benefits.
For more information on benefits, check out our healthcare benefits and additional Box Benefits + Perks .

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