GPU Infrastructure Specialist
- Niagara-on-the-Lake, ON, Canada
Volunteer
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Job details
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- Volunteer
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- Posting language
- English
- Posted
Original job description
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
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.
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.
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.
- Degree in electrical engineering or computer science.
- Experience with GPU architecture, CUDA programming, and hardware monitoring tools.
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.
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.
More about this job
Responsibilities
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.
Skills
- 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
Visa sponsorship
Not detected in the job text
Categories
- Technology
- Engineering
- Science & Research
Keywords
- GPUs
- Hardware Accelerators
- AI Workloads
- GPU Clusters
- GPU-Based Servers
- Multi-GPU Training
- Memory Management
- Load Balancing
- GPU Architecture
- CUDA Programming
- Hardware Monitoring Tools
- Hardware Health
- Large-Scale Experiments
- Electrical Engineering
- Computer Science
- Research Services