Remote Embedded AI Engineer – Edge ML for Mobile & SoCs

Bright Vision Technologies; Centennial (CO); Remote; USD 100,000 - 150,000; Full time

Benefits offered by this job

Remote work

Job summary

Bright Vision Technologies is seeking an Embedded AI Engineer to design, optimize, and deploy ML models for edge devices, including mobile platforms and embedded systems. You will tackle hardware-aware optimization, quantization, and system-level integration to enable AI capabilities outside data centers.

The role requires hands-on experience shipping edge AI in production with constraints on compute, memory, energy, and connectivity.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related field.
• Six or more years of experience in ML engineering, with edge or mobile AI focus.
• 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 (e.g., TensorFlow Lite, ONNX Runtime, Core ML).
• Solid understanding of mobile and embedded hardware architectures.

Responsibilities

• Design and implement edge AI solutions for mobile SoCs, NPUs, and embedded accelerators.
• Apply quantization, pruning, distillation, and architectural optimization for edge models.
• Tune latency, energy efficiency, and memory footprint on target hardware.
• Build cross-platform inference runtimes using TensorFlow Lite, ONNX Runtime, and Core ML.
• Optimize models for accelerator backends including DSPs, NPUs, and mobile GPUs.
• Implement on-device update, versioning, and rollback workflows for large device populations.

Skills

Python

C++

Edge AI

Model compression

ML deployment

Profiling

Education

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

Tools

TensorFlow Lite

ONNX Runtime

Core ML

Job description

Bright Vision Technologies is seeking an Embedded AI Engineer to design, optimize, and deploy ML models for edge devices, including mobile platforms and embedded systems. You will tackle hardware-aware optimization, quantization, and system-level integration to enable AI capabilities outside data centers.

The role requires hands-on experience shipping edge AI in production with constraints on compute, memory, energy, and connectivity.