Remote Edge AI Engineer - Production ML on Mobile/Embedded

Bright Vision Technologies; San Diego, 4S Ranch (CA, CA); À distance; USD 100 000 - 105 000; Plein temps

Avantages offerts par ce poste

Equal Opportunity Employer

Résumé du poste

Bright Vision Technologies is seeking an Edge Computing AI 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 emphasizes model compression, quantization, and hardware-aware optimization, with strong systems engineering to deliver AI at the edge in production environments facing compute, memory, energy, and connectivity trade-offs.

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 significant work on 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 ML models for edge devices.
• Ensure models run efficiently under compute, memory, energy, and connectivity constraints.
• Collaborate with cross-functional teams to ship edge AI capabilities.

Connaissances

Python

C++

Model compression

Quantization

Pruning

Performance profiling

Edge AI

Cross-functional collaboration

Formation

Bachelor’s or Master’s in CS/CE

Outils

Edge inference framework

Description du poste

Bright Vision Technologies is seeking an Edge Computing AI 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 emphasizes model compression, quantization, and hardware-aware optimization, with strong systems engineering to deliver AI at the edge in production environments facing compute, memory, energy, and connectivity trade-offs.