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Posted 14 days ago

Edge/Systems Engineer

@ TAIRC

Niagara-on-the-Lake, Ontario

Volunteer
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Overview

TAIRC is recruiting a volunteer Edge/Systems Engineer in its Applied AI Engineering department to optimize AI models for mobile and edge devices. The role covers embedded systems, hardware accelerators, on-device inference, and securing data and models on devices. The posting describes this as a volunteer contribution with no compensation at this stage.

Responsibilities:

  • Develop edge inference systems and optimize models for mobile and edge devices
  • Collaborate with device manufacturers on efficient inference solutions
  • Design systems for limited memory and compute
  • Implement security measures to protect data and models on devices

Requirements summary:

  • Bachelor's or master's degree in electrical or computer engineering
  • Experience with embedded systems
  • Experience with hardware accelerators
  • Experience with on-device inference

Tools & skills

Source and dates

Source type not verified · apify-linkedin

Employer posting date
2026-10-30
First seen by our source
2026-11-14
Source record updated
2026-11-14

These dates describe the source record, not a live verification that the employer is still hiring. Check the original posting.

Job details

DepartmentEngineering
Work settingOffice, Laboratory
PositionMostly sitting
Physical demandsphysical low · computer high
EducationBachelor’s degree (required), Master’s degree (required)
Fields of studyElectrical engineering, Computer engineering

Summary and details written by AI from the posting; the employer’s posting is the authority.

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Job description
Home/Careers/Edge/Systems Engineer

Applied AI Engineering

Edge/Systems Engineer

These engineers focus on resource-constrained deployments such as edge devices and embedded systems.

Apply for this roleManage an applicationBack to careers

Overview

These engineers focus on resource-constrained deployments such as edge devices and embedded systems.

Responsibilities

  • Develop edge inference systems: Optimize models for mobile and edge devices.
  • Collaborate with hardware teams: Work with device manufacturers to implement efficient inference solutions.
  • Ensure reliability in constrained environments: Design systems for limited memory and compute.
  • Implement security measures: Protect sensitive data and models on devices.

Qualifications

  • Bachelor's or master's degree in electrical engineering or computer engineering.
  • Experience with embedded systems, hardware accelerators, and on-device inference.

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 [email protected]. The application form does not request medical documentation or sensitive identity documents.

Posting information

Department Applied AI Engineering

Public role ID TAIRC-ROLE-021

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.

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