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TAIRC

GPU Infrastructure Specialist

Niagara-on-the-Lake, ON, Canada

Job type: Volunteer
Apply by: Oct 1, 2026
Posting language: English
Posted: Sep 28, 2026

Apply About employer

Applications open in a new tab.

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

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.

Apply About employer

More about this job

Responsibilities

Set up, configure, and maintain GPU-based servers, and optimize GPU utilization through multi-GPU training strategies, memory management, and load balancing. Advise researchers on hardware needs for large-scale experiments, monitor hardware health, and replace 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
GPU Architecture
CUDA Programming
Hardware Monitoring
Hardware Reliability
Research Collaboration

Visa sponsorship

Not detected in the job text

Keywords

GPU
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
Infrastructure & Compute
Research Services

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