Machine Learning Engineer III, Shield
Box · Redwood City, CA
About this role
**Machine Learning Engineer III — Shield (Box)** ## About Box Box (NYSE: BOX) is the leader in Intelligent Content Management. Our platform helps organizations collaborate, manage the full content lifecycle, secure critical content, and transform business workflows with enterprise AI. ## About Box Shield **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. The Shield team is building and scaling ML systems that safeguard millions of users and billions of files from ransomware, data exfiltration, insider threats, and emerging AI-based attacks. ## What you’ll do - **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 processing high-volume security event streams using **Apache Spark**, **GCP Dataflow**, and **Vertex AI**. - **Feature Engineering:** Create and maintain feature stores powering real-time and batch anomaly detection. - **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. - **On-call rotation:** Participate in on-call to help respond to and triage issues. ## Who you are **Must have** - 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; able to explain complex ML concepts to non-technical stakeholders **Nice to have** - Experience in security/threat detection, anomaly detection, or fraud detection - Experience with sequential data, anomaly detection, or behavioral modeling (time-series forecasting, LSTM, Transformers, etc.) - 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 ## Tech stack you’ll work with - **Languages:** Python, Scala, Java - **ML/Data:** Apache Spark, Vertex AI, BigQuery, TensorFlow/PyTorch - **Infrastructure:** GCP, Kubernetes - **Domains:** User Behavior Analytics, Time-Series Anomaly Detection, AI Security, Content classification, Ransomware Detection ## Location / work style Boxers are expected to work from their assigned office **at least 3 days per week**. ## Pay transparency (base salary range) **$175,500 — $219,500 USD** (actual base salary/OTE depends on factors such as knowledge, skill level, experience, and work location). Role is also eligible for equity and benefits. ## Equal opportunity Box is an equal opportunity employer and values diversity. Reasonable accommodations are available for applicants with disabilities. *If you’re excited about the role but don’t meet every requirement, Box encourages you to apply.*
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