Remote Edge AI Engineer: Deploy Production ML on Devices

Bright Vision Technologies; Folsom (CA); Remote; USD 100,000 - 105,000; Full time

Job summary

Bright Vision Technologies in the United States is seeking an Edge Computing AI Engineer to design, optimize, and deploy ML models that run on resource-constrained edge devices, including mobile platforms, embedded systems, and specialized accelerators.

The role demands deep expertise in model compression, quantization, and hardware-aware optimization, plus robust systems engineering to ship reliable AI capabilities outside the data center.

Qualifications

• Bachelor's or Master's in Computer Science, Computer Engineering, or related field.
• Six+ years in ML engineering with edge or mobile AI.
• Proficient in Python and C++.
• Model compression, quantization, pruning experience.
• Experience with at least one major edge inference framework.
• Strong understanding of mobile/embedded hardware architectures.
• Experience deploying ML models to production on mobile/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.
• Implement hardware-aware optimization and profiling.
• Collaborate with cross-functional teams to ship edge AI features.
• Ensure production readiness on mobile/embedded platforms.

Skills

Python

C++

Edge AI

Model compression

Quantization

Pruning

Edge inference

Embedded hardware

Production deployment

Performance profiling

Privacy & security

Cross-functional collaboration

Strong communication

Education

Bachelor's or Master's in CS/CE

Tools

TensorFlow Lite

ONNX

Custom NPU toolchains

DSP toolchains

Job description

Bright Vision Technologies in the United States is seeking an Edge Computing AI Engineer to design, optimize, and deploy ML models that run on resource-constrained edge devices, including mobile platforms, embedded systems, and specialized accelerators.

The role demands deep expertise in model compression, quantization, and hardware-aware optimization, plus robust systems engineering to ship reliable AI capabilities outside the data center.