Edge AI Systems Engineer — Remote

Bright Vision Technologies; Wesley Chapel (FL); Remote; USD 100,000 - 150,000; Full time

Job summary

Bright Vision Technologies is seeking an AI Systems Engineer to design, optimize, and deploy machine learning models that run efficiently on resource-constrained edge devices, including mobile and embedded platforms.

The role requires deep expertise in model compression, quantization, and hardware-aware optimization, plus strong systems engineering skills to ship reliable AI capabilities beyond the data center.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related field.
• Six or more years of experience in ML engineering, with edge or mobile AI.
• Strong proficiency in Python and C++.
• Hands-on experience with model compression, quantization, and pruning techniques.
• Experience with at least one major edge inference framework.
• Solid understanding of mobile and embedded hardware architectures.
• Experience deploying ML models to production on mobile or embedded platforms.
• Strong performance engineering and profiling skills.
• Familiarity with on-device privacy and security considerations.
• Strong communication and cross-functional collaboration skills.

Responsibilities

• Design, optimize, and deploy ML models for edge devices (mobile/embedded).
• Perform model compression, quantization, and hardware-aware optimization.
• Ship reliable AI capabilities outside the data center and collaborate with teams.

Skills

Python

C++

Model compression

Quantization

Pruning

Edge inference framework

Mobile hardware

On-device deployment

Profiling

Privacy on-device

Cross-functional collaboration

Education

Bachelor’s or Master’s degree in Computer Science/Computer Engineering or related field

Job description

Bright Vision Technologies is seeking an AI Systems Engineer to design, optimize, and deploy machine learning models that run efficiently on resource-constrained edge devices, including mobile and embedded platforms.

The role requires deep expertise in model compression, quantization, and hardware-aware optimization, plus strong systems engineering skills to ship reliable AI capabilities beyond the data center.