Develop and optimize embedded software components including time-series databases, stream processing engines, graph analytics modules, and AI inference engines
Design and implement data pipelines for collection, cleaning, and standardization to support AI use cases for network security products (NGFW, HIPS, EDR)
Drive embedded AI system architecture ensuring modularity, scalability, and maintainability; produce high-quality technical documentation
Lead implementation of time-series and graph databases, streaming engines, and correlation engines; support deployment of embedded AI pipelines
Align technical execution with product, algorithm, and testing teams and manage R&D timelines to ensure successful and timely delivery
Requirements
Bachelor’s degree or higher in Computer Science
Experience designing, developing, and optimizing time-series databases (InfluxDB/TimescaleDB/custom)
Experience in firewall/router/switch development
Familiarity with end-to-end embedded AI data processing (collection/cleansing/feature extraction/inference)
Knowledge of cybersecurity product scenarios such as NGFW policy matching, IPS/IDS signature detection
Architectural vision and ability to design modular software architectures
Cross-team coordination and collaboration with product, algorithm, and testing teams
Results-oriented with strong delivery focus and R&D timeline management
Curious about AI for cybersecurity and technological foresight
Bonus: collaboration with cybersecurity vendors
Bonus: open-source contributions or publications (e.g., USENIX Security, Black Hat)
Bonus: understanding of AI applications in NGFW/IPS/EDR contexts