Senior Machine Learning Engineer

January 28

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Dropbox

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Dropbox is a cloud-based service that provides tools for storing, sharing, and accessing files across devices. It offers features such as document sharing, video review, automatic backups, and AI-driven scheduling. Dropbox also provides solutions for different sectors like teams, sales, marketing, and education, and industries including construction, media, technology, and manufacturing. With a focus on security, Dropbox ensures files are encrypted and protected against tampering. It offers integrations with various productivity tools and is trusted by major companies for efficient file management and collaboration.

Cross-platform file sync • File sharing • Online backup • Cloud storage • Collaboration

1001 - 5000 employees

Founded 2007

🏢 Enterprise

⚡ Productivity

📋 Description

• The Senior Machine Learning Engineer will play a pivotal role in supporting the Threat Intelligence and Product Trust & Safety teams by leveraging advanced machine learning techniques to enhance security, detect and prevent abuse, and protect user trust. • This role involves designing, implementing, and maintaining ML models and systems to identify threats, analyze behavioral patterns, and mitigate platform abuse. The ideal candidate has a strong foundation in machine learning, data science, and software engineering, with a passion for security and product trust. • Many teams at Dropbox run Services with on-call rotations, which entails being available for calls during both core and non-core business hours. If a team has an on-call rotation, all engineers on the team are expected to participate in the rotation as part of their employment.

🎯 Requirements

• Bachelor’s, Master’s, or Ph.D. in Computer Science, Machine Learning, Data Science, or a related technical field • 8+ Years experience designing, building, and deploying ML models for security-related use cases such as anomaly detection, behavior analysis, predictive modeling, and adversarial threat detection • Experience developing ML-driven real-time detection systems using tools like Apache Kafka, AWS Kinesis, or Google Pub/Sub • Proficiency with graph-based ML models, clustering techniques, and graph neural networks (GNNs) for detecting coordinated malicious activities • Proficiency in Python, Scala, or Java for developing and deploying ML solutions • Familiarity with scalable data systems (e.g. Databricks, Spark, data lakes) and with systems such as binary and function signals • Familiarity with security domains such as phishing detection and account takeover prevention