Edge AI Systems Engineer - Remote

Bright Vision Technologies; Orlando (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 ML models for edge devices, including mobile and embedded platforms. The role emphasizes model compression, quantization, and hardware-aware optimization.

You will ship production edge AI, collaborate with cross-functional teams across product and engineering, and address constraints in compute, memory, energy, and connectivity while ensuring on-device privacy and security.

Qualifications

• Degree in CS/CE or related field.
• 6+ years of ML engineering experience with edge or mobile AI.
• Proficient in Python and C++.
• Experience with model compression, quantization, pruning.
• Experience with at least one major edge inference framework.
• Knowledge of mobile/embedded hardware architectures.
• Experience deploying ML models to production on mobile/embedded platforms.
• Strong performance engineering and profiling skills.
• Awareness of on-device privacy and security considerations.
• Strong communication and cross-functional collaboration.

Responsibilities

• Design, optimize, and deploy ML models for edge devices.
• Ensure models run efficiently on mobile and embedded hardware.
• Collaborate with product and engineering teams across domains.
• Prioritize model performance, memory, energy, and connectivity trade-offs.
• Advise on hardware-aware optimization strategies.

Skills

Python

C++

ML engineering

Edge computing

Performance profiling

Education

Bachelor’s or Master’s degree in Computer Science or Computer Engineering

Tools

Edge inference framework

Job description

Bright Vision Technologies is seeking an AI Systems Engineer to design, optimize, and deploy ML models for edge devices, including mobile and embedded platforms. The role emphasizes model compression, quantization, and hardware-aware optimization.

You will ship production edge AI, collaborate with cross-functional teams across product and engineering, and address constraints in compute, memory, energy, and connectivity while ensuring on-device privacy and security.