Edge Computing AI Engineer

Bright Vision Technologies • San Diego, CA

3d • $100,000 - $105,000 • Remote

About The Position

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, embedded systems, and specialized accelerators. The role calls for expertise in model compression, quantization, and hardware-aware optimization, as well as systems engineering skills to deliver reliable AI capabilities outside the data center. The ideal candidate has shipped edge AI in production environments where compute, memory, energy, and connectivity constraints shape engineering trade-offs.

This is a 100% remote, full-time, direct W2 position in the United States. The stated annual salary range is $100,000–$105,000. Bright Vision Technologies is an equal opportunity employer and prohibits workplace harassment and discrimination.

Requirements

• 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.

Nice To Haves

• Experience with custom NPU or DSP toolchains.
• Familiarity with federated learning or on-device personalization.
• Exposure to safety-critical or industrial edge deployments.
• Open-source contributions to edge AI frameworks.
• Experience optimizing LLMs for on-device inference.

Responsibilities

• Design, optimize, and deploy machine learning models for resource-constrained edge devices.
• Apply model compression, quantization, and hardware-aware optimization to enable efficient edge inference.
• Use systems engineering and performance optimization to deliver reliable AI capabilities outside the data center.
• Deploy machine learning models to production on mobile or embedded platforms.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior