Principal Engineer – Embedded AI SW for Network

Principal Engineer – Embedded AI Software for Network Devices

Responsibilities

Core System Development for Network Devices

• Lead embedded system software development and optimization, focusing on time-series databases, stream processing engines, correlation analysis engines, graph databases, and AI inference engine design.
• Develop a governance framework for network device time-series data (e.g., traffic logs, security events, system logs, RF data, system status) and design efficient storage, query, and real-time analytics solutions.
• Identify and resolve performance bottlenecks in high-concurrency scenarios to ensure system stability and low-latency response.

Security AI Solution Design

• Design data collection, cleansing, and standardization pipelines tailored for security applications (NGFW, HIPS, IPS, EDR).
• Optimize data pipelines and model inference interfaces in collaboration with AI algorithm teams to enhance end-to-end efficiency.

Architecture Design & Implementation

• Develop embedded AI system architectures and deliver modular, implementable solutions with detailed technical documentation.
• Define technology evolution roadmaps, advancing key technologies from research to production.

Cross-Team Collaboration & Delivery

• Align technical solutions with broader business objectives across product, algorithm, and testing teams.
• Manage R&D timelines to ensure high-quality project execution.

Requirements

Mandatory Qualifications

Education & Experience:

• Master’s degree or higher in Computer Science.
• 10+ years of experience in C development.
• 6+ years in network device development.
• 5+ years in architecture design.

Technical Skills:

• Expertise in developing, designing, and optimizing time-series databases (InfluxDB, TimescaleDB, or custom solutions).
• Strong background in firewall, router, and switch development.
• Experience with end-to-end embedded AI data processing, including collection, cleansing, feature extraction, and inference.
• Knowledge of security product scenarios such as NGFW, IDS, and IPS.

Soft Skills:

• Architectural Thinking: Ability to translate business needs into technical architectures and break them down into actionable tasks.
• Proactive Coordination: Strong cross-functional collaboration skills to resolve conflicts and drive project completion.
• Results-Driven: High attention to deadlines and efficient execution capabilities.
• Technical Foresight: Deep interest in AI and cybersecurity trends, particularly AI-driven threat detection.

Preferred Qualifications

• Experience working with cybersecurity vendors and endpoint protection solutions.
• Familiarity with PyTorch/TensorFlow model deployment optimization or AI inference engine development.
• Contributions to open-source projects or publications in top conferences (e.g., USENIX Security).
• Understanding of cybersecurity scenarios, including NGFW policy matching, IPS signature detection, and EDR behavioral analysis.