Senior Machine Learning Engineer, Dropbox

• The ideal candidate has a strong foundation in machine learning, data science, and software engineering, with a passion for security and product trust
• 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

Desirable

• 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
• Design, build, and deploy machine learning models to detect and mitigate security threats, such as account takeovers, phishing, and malicious content distribution
• Develop algorithms for anomaly detection, behavior analysis, and predictive modeling to proactively identify risks and abuse patterns
• Develop graph, cluster and other adversarial risk signals for detecting and enforcing on bulk and coordinated operation among Dropbox accounts
• Work closely with Threat Intelligence, Product Trust & Safety, and Security Engineering teams to define and prioritize ML projects aligned with organizational goals
• Partner with data scientists, software engineers, and security analysts to integrate ML models into existing workflows and platforms
• Analyze large, complex datasets from multiple sources, including user behavior, telemetry, and external threat intelligence feeds
• Develop ML-driven solutions for real-time threat detection and response, including automation of security workflows
• Collaborate on initiatives to enhance user safety, such as URL reputation scoring, and abuse prevention