GPU Infrastructure Specialist

Job summary

Set up, configure, and maintain GPU-based servers, and optimize GPU utilization through multi-GPU training, memory management, and load balancing. Advise researchers on hardware needs for large-scale experiments and monitor hardware health, replacing components as needed.

Job details

Home/Careers/GPU Infrastructure Specialist Infrastructure & Compute GPU Infrastructure Specialist These specialists focus on GPUs and hardware accelerators used for AI workloads. Apply for this roleManage an applicationBack to careers Overview These specialists focus on GPUs and hardware accelerators used for AI workloads. Responsibilities Manage GPU clusters: Set up, configure, and maintain GPU-based servers. Optimize GPU utilization: Implement strategies for multi-GPU training, memory management, and load balancing. Collaborate with researchers: Provide advice on hardware requirements for large-scale experiments. Ensure reliability: Monitor hardware health and replace components as needed. Qualifications Degree in electrical engineering or computer science. Experience with GPU architecture, CUDA programming, and hardware monitoring tools. Equal opportunity TAIRC evaluates candidates using role-related evidence and documented review criteria. Employment decisions must not be based on protected personal characteristics. Engagement pathways and compensation status TAIRC is currently building its funding base. Opportunities identified as volunteer or unpaid do not provide compensation at this stage. Any future paid appointment would require available funding, organizational approval, applicable legal compliance, and a separate written agreement. Participation does not guarantee future employment or compensation. Volunteer contribution interest may be reviewed only as a voluntary, scoped pathway. Educational internship interest requires separate supervision, educational purpose, and legal review. Advisory expressions of interest are not board or formal advisory appointments. Future compensated employment is contingent on funding, approval, legal compliance, and a separate written offer. Accessibility and accommodations For an accommodation or alternate submission format, contact hr@tairc.com. The application form does not request medical documentation or sensitive identity documents. Posting information Department Infrastructure & Compute Public role ID TAIRC-ROLE-039 Status Open Location, employment type, workplace type, compensation, sponsorship policy, posted date, and closing date are omitted unless HR has entered approved factual values. Recruitment privacy Application data is used for recruitment review, stored with restricted access, and handled under the recruitment privacy and retention policy.

What you’ll do

Set up, configure, and maintain GPU-based servers, and optimize GPU utilization through multi-GPU training, memory management, and load balancing. Advise researchers on hardware needs for large-scale experiments and monitor hardware health, replacing components as needed.

Requirements

A degree in electrical engineering or computer science is required. Candidates should have experience with GPU architecture, CUDA programming, and hardware monitoring tools.

Listed skills

• Health · Preferred
• management · Preferred
• Training · Preferred
• future · Preferred
• Recruitment · Preferred
• Compliance · Preferred
• Documentation · Preferred
• Reliability · Preferred
• Retention · Preferred
• Supervision · Preferred
• Organization · Preferred
• Attention to detail · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• GPU Cluster Management
• GPU Server Configuration
• GPU Utilization Optimization
• Multi-GPU Training
• Memory Management
• Load Balancing
• CUDA Programming
• GPU Architecture
• Hardware Monitoring
• Hardware Reliability
• Research Collaboration

Job areas

• Technology
• Engineering
• Science & Research

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