Edge AI Engineer

Bright Vision Technologies • Aurora, IL

1d • $70,000 - $100,000 • Remote

About The Position

Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. The company describes this as an opportunity to join an established organization with career growth potential.

The Edge AI Engineer will 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 requires 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 full-time, direct W-2 position that is 100% remote in the United States. The stated salary range is $70,000–$100,000 annually. The employer welcomes applications from U.S. citizens, Green Card holders, EAD holders, and H-1B transfer candidates, but cannot sponsor new H-1B visa petitions.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related field.
• At least six 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 machine learning models for efficient operation on resource-constrained edge devices.
• Optimize and deploy machine learning models on mobile platforms, embedded systems, and specialized accelerators.
• Apply model compression, quantization, pruning, and hardware-aware optimization techniques.
• Develop and ship reliable AI capabilities outside the data center.

What This Job Offers

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