Remote Edge AI Engineer: ML on Resource-Constrained Devices

Bright Vision Technologies; USA; Remote; USD 100.000 - 105.000; Vollzeit

Zusammenfassung

Bright Vision Technologies is seeking an Edge Computing AI Engineer to design, optimize, and deploy models that run on edge devices, including mobile and embedded platforms. The role emphasizes model compression, quantization, and hardware-aware optimization, paired with systems engineering to deliver AI outside the data center.

The ideal candidate has shipped edge AI in production with tight compute and energy constraints and strong cross-functional collaboration skills.

Qualifikationen

• Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related field.
• Six+ years of experience in ML engineering with edge or mobile AI.
• Strong proficiency in Python and C++.
• Hands-on experience with model compression, quantization, and pruning.
• 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/embedded platforms.
• Strong performance engineering and profiling skills.
• Familiarity with on-device privacy and security considerations.
• Strong communication and cross-functional collaboration skills.

Aufgaben

• Design, optimize, and deploy ML models on resource-constrained edge devices.
• Engineer hardware-aware optimizations for efficient on-device inference.
• Collaborate with mobile/embedded teams to ship production edge AI.
• Profile, test, and validate performance and energy usage.

Kenntnisse

Python

C++

Model compression

Quantization

Pruning

Edge inference

Profiling

Privacy & security

Cross-functional communication

Ausbildung

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

Tools

Edge inference framework

Jobbeschreibung

Bright Vision Technologies is seeking an Edge Computing AI Engineer to design, optimize, and deploy models that run on edge devices, including mobile and embedded platforms. The role emphasizes model compression, quantization, and hardware-aware optimization, paired with systems engineering to deliver AI outside the data center.

The ideal candidate has shipped edge AI in production with tight compute and energy constraints and strong cross-functional collaboration skills.