Intelligent Edge Engineer

Bright Vision Technologies • Wildwood, MO

1d • $135,000 - $155,000 • Remote

About The Position

Bright Vision Technologies is seeking an Intelligent Edge 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 full-time, direct W2 position that is 100% remote within the United States. The salary range is $135,000–$155,000 annually. Applicants must be authorized to work in the United States; U.S. citizens, Green Card holders, EAD holders, and H-1B transfer candidates are encouraged to apply. The company cannot sponsor new H-1B visa petitions for this position.

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.
• Use model compression, quantization, and hardware-aware optimization to support efficient edge inference.
• Ship reliable AI capabilities on mobile platforms, embedded systems, and specialized accelerators.
• Deploy ML models to production on mobile or embedded platforms.
• Profile and optimize performance under compute, memory, energy, and connectivity constraints.

What This Job Offers

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