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Edge/Systems Engineer
- Niagara-on-the-Lake, ON, Canada
Volunteer
This opening has closed.
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- Job type
- Volunteer
- Closing date
- Posting language
- English
- Posted
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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
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 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.
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.
- Bachelor's or master's degree in electrical engineering or computer engineering.
- Experience with embedded systems, hardware accelerators, and on-device inference.
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 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.
More about this job
Responsibilities
Develop and optimize inference systems and models for mobile, edge, and embedded devices with limited memory and computing resources. Collaborate with hardware teams to implement efficient solutions and protect sensitive data and models on devices.
Requirements
A bachelor's or master's degree in electrical engineering or computer engineering is required. Candidates should have experience with embedded systems, hardware accelerators, and on-device inference.
Skills
- Edge Inference
- Model Optimization
- Mobile Devices
- Embedded Systems
- Hardware Accelerators
- On-Device Inference
- Hardware Collaboration
- Resource-Constrained Systems
- Memory Optimization
- Compute Optimization
- System Reliability
- Data Security
- Model Security
Visa sponsorship
Not detected in the job text
Categories
- Engineering
- Technology
- Software
- Science & Research
Keywords
- Applied AI Engineering
- Edge Devices
- Embedded Systems
- Edge Inference
- Model Optimization
- Mobile Devices
- Hardware Accelerators
- On-Device Inference
- Limited Memory
- Limited Computing Resources
- System Reliability
- Data Security
- Model Security
- Electrical Engineering
- Computer Engineering
- Research Services