Embedded AI SW (Network) - Paris

Lead embedded system software development and optimization, including time-series databases, stream processing engines, correlation analysis engines, graph databases, and AI inference engine design.

Establish 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.

Resolve performance bottlenecks in high-concurrency scenarios to ensure system stability and low-latency response.

2. Security AI Solution Design

Design data collection, cleansing, and standardization pipelines aligned with product requirements (NGFW/HIPS/IPS/EDR) to support AI application architectures.

Collaborate with AI algorithm teams to optimize data pipelines and model inference interfaces for end-to-end efficiency.

3. Architecture Design & Implementation

Drive embedded AI system architecture design and deliver modular, implementable solutions with technical documentation.

Define technology evolution roadmaps and advance key technologies from research to commercialization.

4. Cross-Team Collaboration & Delivery

Align technical solutions and business objectives across product/algorithm/testing teams.

Manage R&D timelines to ensure high-quality project delivery.

Requirements

1. Mandatory Qualifications

Education & Experience: Master’s degree or higher in Computer Science, 10+ years of C development experience, 6+ years in network device development, 5+ years in architecture design.

Technical Skills:

·        Hands-on experience in developing, designing, and optimizing time-series databases (InfluxDB/TimescaleDB/custom).

·        Expertise in firewall/router/switch development.

·        Familiarity with end-to-end embedded AI data processing (collection/cleansing/feature extraction/inference).

·        Knowledge of security product scenarios (NGFW/IDS/IPS).

2. Soft Skills

·        Architectural Thinking: Ability to abstract technical architectures from business needs and decompose them into actionable tasks.

·        Proactive Coordination: Strong cross-functional collaboration skills to resolve conflicts and drive project closure.

·        Results-Driven: High sensitivity to deadlines and efficient execution capabilities.

·        Technical Foresight: Keen interest in AI and cybersecurity trends (e.g., AI-driven threat detection).

3. Preferred Qualifications

·        Experience in collaborating with cybersecurity vendors and endpoint protection solution providers.

·        Familiarity with PyTorch/TensorFlow model deployment optimization or AI inference engine development.

·        Leadership in open-source projects or publications in top conferences (e.g., USENIX Security).

·        Knowledge of cybersecurity scenarios: NGFW policy matching/IPS signature detection/EDR behavioral analysis.

Advantages

·  Technical Challenges: Design large-scale data processing architectures (millions of devices) and collaborate with global security experts.

· Career Growth: Flexible promotion path; top performers may advance to Lab Director roles.

· Compensation: Competitive salary + performance-based bonuses.