Edge ML Engineer

Bright Vision Technologies • Nashville, TN

1d • $100,000 - $150,000 • 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 ML 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 involves model compression, quantization, and hardware-aware optimization, as well as systems engineering 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 W2 position that is 100% remote within the United States. The stated annual salary range is $100,000–$150,000, and the position requires six or more years of experience.

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, including mobile platforms, embedded systems, and specialized accelerators.
• Apply model compression, quantization, and hardware-aware optimization to support efficient edge inference.
• Use systems engineering practices to ship reliable AI capabilities outside the data center.
• Deliver edge AI in production environments while accounting for compute, memory, energy, and connectivity constraints.

What This Job Offers

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