Edge ML Engineer - Remote, Production AI on Devices

Bright Vision Technologies; United States; Remote; USD 100,000 - 150,000; Full time

Job summary

Bright Vision Technologies is seeking an Edge ML Engineer to design, optimize, and deploy models that run on resource-constrained edge devices, including mobile platforms and embedded systems. The role requires expertise in model compression, quantization, and hardware-aware optimization, along with strong systems engineering to ship AI outside the data centre.

The ideal candidate has shipped edge AI in production where compute, memory, energy, and connectivity constraints shape engineering

Qualifications

• Bachelor’s or Master’s degree in CS/CE or a related field.
• 6+ years of ML engineering experience 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 edge ML models for resource-constrained devices.
• Focus on model compression, quantization, and hardware-aware optimization.
• Work across mobile platforms, embedded systems, and specialised accelerators.
• Collaborate with cross-functional teams to ship reliable AI capabilities.

Skills

Edge ML

Model compression

Quantization

Pruning

Edge inference

Systems engineering

On-device privacy

Cross-functional collaboration

Python

C++

Education

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

Tools

Python

C++

Job description

Bright Vision Technologies is seeking an Edge ML Engineer to design, optimize, and deploy models that run on resource-constrained edge devices, including mobile platforms and embedded systems. The role requires expertise in model compression, quantization, and hardware-aware optimization, along with strong systems engineering to ship AI outside the data centre.

The ideal candidate has shipped edge AI in production where compute, memory, energy, and connectivity constraints shape engineering