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The DNS Security Research team delivers high-quality content to our products to prevent successful cyberattacks, focusing especially on DNS security, IP layer defense, and network security. We design, build, and maintain various detection systems and data infrastructures to protect customers against constantly evolving threats and attacks.
Job Responsibility:
Track and research emerging threats and innovate new ways to identify malicious indicators used by malware and attacks, including domains, URLs, IP addresses, sha256, email addresses, etc.
Design and build scalable and extensible prevention/detection systems
Leverage data-driven approaches, such as statistical analysis, machine learning, and other advanced techniques
Convert research results and discoveries into products, research papers, etc.
Requirements:
PhD in Computer Science, Cyber Security or Machine Learning or equivalent experience
2+ years of Security research experience
Creative thinker and team player. Have great passion and be highly self-motivated in data-driven security research
Expertise in DNS and IPv4/IPv6
Good knowledge of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Trees, Logistic Regression, Deep Learning, and Boosting
Familiar with large-language models (LLMs) and experience to leverage them to address cybersecurity threats
Excellent programming skills in Python, Shell script, Go, or SQL
Understanding of core network protocols (TCP/IP, HTTP/HTTPS, etc.)
Knowledge and experience with modern databases and big data tools, such as MySQL, MongoDB, Elasticsearch, Redis, BigQuery
Be comfortable working independently, efficiently
Excellent communication (written and verbal) and presentation skills
Nice to have:
Experience with malware traffic analysis is a plus
Experience with graph database (e.g. Neo4j, Amazon Neptune), graph-based analysis is a plus
Experience with Google Cloud Platform is a plus
Proven success (past publication record, open source project contributions) is a big plus