Remote Edge AI Engineer - Production-Ready ML on Edge

Bright Vision Technologies; Cranberry Township (Butler County); Remote; USD 100,000 - 155,000; Full time

Job summary

Bright Vision Technologies in the United States is seeking an Edge AI Engineer to design, optimize, and deploy ML models on edge devices, including mobile platforms and embedded systems.

You will implement model compression techniques, quantization, and hardware-aware optimization, build cross-platform runtimes, and collaborate with hardware and product teams to deliver reliable edge AI capabilities for diverse devices.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or related field.
• 6+ years of ML engineering experience with edge or mobile AI.
• Proficiency in Python and C++.
• Hands-on 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.

Responsibilities

• Design edge AI solutions optimized for diverse hardware (mobile SoCs, NPUs, embedded accelerators).
• Apply quantization, pruning, distillation, and architectural optimization to fit edge constraints.
• Tune models for latency, energy, and memory on target hardware.
• Build cross-platform inference runtimes (TensorFlow Lite, ONNX Runtime, Core ML).
• Optimize models for accelerator backends (DSPs, NPUs, mobile GPUs).
• Implement on-device model updates, versioning, and safe rollout workflows.

Skills

Python

C++

Edge AI

Model compression

Performance profiling

Education

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

Tools

TensorFlow Lite

ONNX Runtime

Core ML

Job description

Bright Vision Technologies in the United States is seeking an Edge AI Engineer to design, optimize, and deploy ML models on edge devices, including mobile platforms and embedded systems.

You will implement model compression techniques, quantization, and hardware-aware optimization, build cross-platform runtimes, and collaborate with hardware and product teams to deliver reliable edge AI capabilities for diverse devices.