Edge AI Engineer - Remote, Production-Ready

Bright Vision Technologies

Wildwood (MO)

Remote

USD 135,000 - 155,000

Full time

6 days ago
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Job summary

Bright Vision Technologies is seeking an Intelligent Edge Engineer to design, optimize, and deploy ML models on resource-constrained edge devices, including mobile platforms and embedded systems. The role requires deep expertise in model compression, quantization, hardware-aware optimization, and strong systems engineering to ship AI capabilities outside the data center.

Ideal candidates have shipped edge AI in production with careful attention to 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 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.

Responsibilities

  • Design, optimize, and deploy ML models on edge devices.
  • Address compute, memory, energy, and connectivity constraints.
  • Collaborate with cross-functional teams to ship AI capabilities outside the data center.

Skills

Python
C++
Edge ML
Model compression
Quantization
Pruning
Edge frameworks
Mobile hardware
Mobile deployment
Profiling
On-device privacy

Education

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

Job description

Bright Vision Technologies is seeking an Intelligent Edge Engineer to design, optimize, and deploy ML models on resource-constrained edge devices, including mobile platforms and embedded systems. The role requires deep expertise in model compression, quantization, hardware-aware optimization, and strong systems engineering to ship AI capabilities outside the data center.

Ideal candidates have shipped edge AI in production with careful attention to compute, memory, energy, and connectivity

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