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Senior Machine Learning Engineer, Strategic Intelligence Group

Posted 2 months agoViewed

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💎 Seniority level: Senior, 8+ Years

📍 Location: Canada

💸 Salary: 199800.0 - 270300.0 CAD per year

🔍 Industry: Software Development

🏢 Company: Dropbox👥 1001-5000💰 $600,000,000 Debt Financing about 8 years ago🫂 Last layoff almost 2 years agoPrivate CloudWeb HostingFile SharingEnterprise SoftwareCollaboration

⏳ Experience: 8+ Years

🪄 Skills: PythonCybersecurityData AnalysisJavaMachine LearningAlgorithmsApache KafkaData engineeringData scienceSparkRESTful APIsScalaData modelingSoftware Engineering

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
Responsibilities:
  • 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.
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