Similar Jobs at Dropbox
The 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.
Our Engineering Career Framework is viewable by anyone outside the company and describes what’s expected for our engineers at each of our career levels. Check out our blog post on this topic and more here.
- 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.
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. Applicants are encouraged to ask for more details of the rotations to which the applicant is applying.
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.
- Experience applying machine learning techniques to security-focused problems such as anomaly detection, phishing prevention, and account takeover mitigation.
- Strong understanding of ML algorithms for behavior analysis, predictive modeling, and real-time threat detection.
- Strong collaborative skills with cross-functional teams, including data scientists, engineers, and security analysts, to integrate ML solutions into workflows.
- Demonstrated ability to design, deploy, and optimize production-level ML systems in high-impact areas.
- Excellent problem-solving, analytical, and communication skills with a passion for building secure, user-centric solutions.
