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# Senior Machine Learning Engineer, Shield

- **Company:** Box, Inc.
- **Location:** Redwood City, CA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Artificial Intelligence, Amazon Web Services, BigQuery, Computer Programming, Data Flow Control, Fraud Prevention and Detection, Intrusion Detection and Prevention, Python (Programming Language), Machine Learning, Tensorflow, Cloudera, Azure Machine Learning, Data Streaming, Feature Store, Google Cloud, Feature Engineering, Pytorch, Apache Spark, Model Validation, Kubernetes, Information Technology, Machine Learning Operations, Data Pipelines, Apache Beam, Recurrent Neural Networks, Golang
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25303_f48f2825e0a879a31ea64dfead23fd7f

## About the Role

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, BigQuery, Dataflow) or equivalent (AWS SageMaker, 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., * Bachelors or above degree in Computer Science or equivalent practical experience.
 * Strong programming skills in Python.
 * Deployed and maintained ML models serving real traffic.
 * Deep understanding of feature engineering, model evaluation, and MLOps.
 * Clear, inclusive communicator who values collaboration, mentorship, and continuous improvement., * Experience in security/threat or fraud detection and with sequential data and behavioral modeling (e.g., anomaly detection, time-series forecasting, LSTM, Transformers, or similar).
 * Experience with streaming/real-time ML systems.
 * Experience with FedRAMP/compliance-constrained environments.
 * Familiarity with Java stack for service integrations.
 * Publications or contributions in ML security.

Tech Stack You'll Work With

 * Languages: Python, Go, 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.

## Description

* Build Threat Detection Models: Design, train, and deploy ML models for ransomware detection, suspicious session identification, user behavior analytics, and 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, BigQuery 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., Box makes reasonable accommodations for applicants with disabilities. If a reasonable accommodation is needed to participate in the job application or interview process, please complete this form. Reasonable accommodations may include scheduling adjustments, document dictation and beyond.

Notice to applicants in Los Angeles: Box, Inc and its related branches will consider for employment, qualified applicants with criminal histories in a manner consistent with the Los Angeles Fair Chance Ordinance. The Fair Chance Ordinance is provided here.

Notice to applicants in San Francisco: Box, Inc and its related branches will consider for employment, qualified applicants with criminal histories in a manner consistent with the San Francisco Fair Chance Ordinance. The Fair Chance Ordinance is provided here.