Remote Intelligent Edge Engineer (ML on Edge Devices)

Bright Vision Technologies; États-Unis; À distance; USD 135 000 - 155 000; Plein temps

Résumé du poste

Bright Vision Technologies seeks an Intelligent Edge Engineer to design, optimize, and deploy machine learning models that run efficiently on resource-constrained edge devices, including mobile platforms and embedded systems. The role focuses on model compression, quantization, and hardware-aware optimization, paired with strong systems engineering to deliver AI capabilities beyond the data center.

The ideal candidate has shipped edge AI in production, with attention to compute, memory, energy,

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related field.
• Six+ 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.

Responsabilités

• Design, optimize, and deploy edge ML models on mobile/embedded platforms.
• Engineer hardware-aware ML solutions balancing compute, memory, energy, and connectivity.
• Collaborate with cross-functional teams to ship AI capabilities outside the data center.

Connaissances

Python

C++

Model compression

Quantization

Pruning techniques

Edge AI

Systems engineering

Production deployment

Profiling

On-device privacy

Formation

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

Outils

TensorFlow Lite

ONNX

NPU toolchains

Description du poste

Bright Vision Technologies seeks an Intelligent Edge Engineer to design, optimize, and deploy machine learning models that run efficiently on resource-constrained edge devices, including mobile platforms and embedded systems. The role focuses on model compression, quantization, and hardware-aware optimization, paired with strong systems engineering to deliver AI capabilities beyond the data center.

The ideal candidate has shipped edge AI in production, with attention to compute, memory, energy,