Machine Learning Engineer III, Shield

Box•Redwood City, CA

4h•$175,500 - $219,500•Hybrid

About The Position

Box is hiring a Machine Learning Engineer for its Shield team, which builds security capabilities to protect enterprise content and reduce content-centric risk. The role focuses on developing and scaling machine learning systems that help safeguard users and files from ransomware, data exfiltration, insider threats, and emerging AI-based attacks. It is part of a fast-paced backend and core team and involves collaboration across Box to deliver security capabilities for high-demand use cases. The engineer will design, train, and deploy threat-detection models; own end-to-end machine learning pipelines for high-volume security event streams; build feature stores for real-time and batch anomaly detection; and deploy, monitor, and improve production ML models serving enterprise customers. The position collaborates with Platform, Application Engineering, and Product teams to translate security needs into ML solutions and participates in an on-call rotation, remaining available while on call to respond to and triage issues. Box expects employees to work from their assigned office at least three days per week. The role is eligible for equity and benefits. The Redwood City base salary range is $175,500–$219,500 USD.

Requirements

At least 3 years of experience in applied machine learning.
Strong programming skills in Python.
Experience deploying and maintaining ML models serving real traffic.
Experience with GCP (Vertex AI, BigQuery, Dataflow) or equivalent platforms such as AWS SageMaker or Azure ML.
Deep understanding of feature engineering, model evaluation, and MLOps.
Strong communication skills, including the ability to explain complex ML concepts to non-technical stakeholders.

Nice To Haves

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

Responsibilities

Design, train, and deploy machine learning 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, and Vertex AI.
Create and maintain feature stores for real-time and batch anomaly detection systems.
Deploy, monitor, and iterate on production ML models serving enterprise customers at scale.
Partner with Platform, Application Engineering, and Product teams to translate security requirements into ML solutions.
Participate in the on-call rotation and respond to and triage issues while on call.