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負責人王虹穎
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統編90324753
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人數11 - 50 人
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資本額700 萬
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地址臺北市信義區暫不公開
In 2025, IQronix launched as a specialized deep-tech division, focused on accelerating the productization and scalable deployment of embedded systems—across kernel, firmware, and advanced edge AI—for leading technology companies.
Our foundation is delivery, not just design. We bring essential system stability and scalability to mission-critical platforms.
Code of Honor
· Integrity · Ownership · Deliver Excellence · Accountability · Transparency · Speak-up Culture
Competitive compensation with 13-month salary
Bonuses for major traditional festivals (Chinese New Year, Dragon Boat Festival, Mid-Autumn Festival)
National Labor Insurance and National Health Insurance in accordance
Additional group insurance coverage
Leave policy more favorable than the Labor Standards Act
Flexible working hours
One day of work from home per week
Comfortable and supportive working environment
Occasional team gatherings and company events
Core Responsibilities 1. Test Engineering & Migration Drive Architecture Upgrades: Participate in the technical maintenance and new feature development of the ARTS framework, leading the configuration unification and migration from legacy versions to the new version. Cross-Platform Integration: Perform test integration for various underlying chipset frameworks and solve technical challenges arising from hardware differences. Develop Specialized Test Modes: Implement new features such as Non-LLM execution modes and audio testing capabilities. 2. Omnilab Operations High Availability Management: Operate and maintain the Omnilab automation platform, ensuring the server achieves a 99.9% uptime target. Hardware Resource Optimization: Proactively identify and resolve laboratory hardware bottlenecks and manage the configuration of diverse AI test devices. 3. Model Quality & Visibility Automated Monitoring Development: Build and maintain data visualization dashboards to monitor key KPIs such as scores, model names, and runtimes. Metric Benchmarking: Execute advanced model evaluation metrics and conduct baseline comparison analysis. 4. Non-Functional Validation Hardware Performance Evaluation: Validate on-device resource utilization (Memory, CPU, and Thermal) during AI tasks to ensure test stability under high-load scenarios.