Hardware / Systems Engineer (Infrastructure) - R&D

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Key Responsibilities:

• Assist in setup, monitoring, and maintenance of GPU-based research server systems and network hardware.
• Manage system configurations, power/cooling requirements, and connectivity setups for compute clusters.
• Coordinate with vendors and support teams for hardware servicing, upgrades, and troubleshooting.
• Maintain inventory of research infrastructure assets, ensuring performance and reliability.
• Implement and monitor power backup, air cooling, and data-center grade best practices.
• Support secure access, uptime monitoring, and compliance for mission-critical research systems.
• Participate in internal efforts to build and benchmark custom server infrastructure tailored for large-scale AI model training.
• Contribute to ongoing infrastructure research, including system optimization, hardware-software integration, and experimental rack-level design.
• Support the evaluation, research, and deployment of the latest hardware stacks like ADA lovelace, Blackwell architecture for pretraining our foundation model.

Requirements:

• Bachelors degree or diploma in Electronics, Computer Science, Information Technology, or a related field.
• High logical reasoning and a strong desire to understand system architecture and AI infrastructure.
• Basic knowledge of computer hardware, networking, and server maintenance.
• Willingness to work hands-on with physical infrastructure and troubleshoot hardware-level issues.
• Strong communication skills and a collaborative mindset to work with researchers and engineers.

Added Advantage:

• Familiarity with Linux environments or system utilities is a plus.
• Exposure to server clusters, GPU management, BIOS configurations, or research compute environments is advantageous.

Why Join Blubridge:

• Directly contribute to AI research infrastructure.
• Collaborate with researchers working on the advance of LLM architecture and scaling.
• Work hands-on in building our own custom compute infrastructure optimized for our training model.
• Gain rare experience with high-performance compute environments, including NVIDIA latest Blackwell-based systems.
• Be part of a curious, driven, and research-oriented team building deep systems capability.