Remote Edge AI Engineer - Mobile/Embedded ML

Bright Vision Technologies; United States; Remote; USD 135,000 - 155,000; Full time

Benefits offered by this job

Equal Opportunity Employer

Job summary

Bright Vision Technologies is seeking an Intelligent Edge Engineer to design, optimize, and deploy ML models for resource-constrained edge devices, including mobile platforms, embedded systems, and specialized accelerators.

The role emphasizes model compression, quantization, and hardware-aware optimization, with strong systems engineering to ship AI capabilities outside the data center and production deployment experience.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or 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 resource-constrained edge devices.
• Perform hardware-aware optimization and profiling for edge deployments.
• Ship reliable AI capabilities outside the data center in production environments.
• Collaborate with cross-functional teams to meet performance and privacy requirements.

Skills

Python

C++

Edge AI

Model compression

Quantization

Pruning

Production deployment

Education

Bachelor’s or Master’s degree in CS/CE or related field

Tools

Edge inference framework

NPU/DSP toolchains

Job description

Bright Vision Technologies is seeking an Intelligent Edge Engineer to design, optimize, and deploy ML models for resource-constrained edge devices, including mobile platforms, embedded systems, and specialized accelerators.

The role emphasizes model compression, quantization, and hardware-aware optimization, with strong systems engineering to ship AI capabilities outside the data center and production deployment experience.