Edge Computing AI Engineer

Bright Vision Technologies•Sunnyvale, CA

4d•$100,000 - $150,000•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. This role involves working with mobile platforms, embedded systems, and specialized accelerators. The ideal candidate will have deep expertise in model compression, quantization, and hardware-aware optimization, coupled with strong systems engineering skills to deploy AI capabilities outside the data center. Experience shipping edge AI in production environments, considering compute, memory, energy, and connectivity constraints, is essential.

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 edge devices.
Ensure efficient model performance on resource-constrained edge devices (mobile platforms, embedded systems, specialized accelerators).
Apply model compression, quantization, and hardware-aware optimization techniques.
Utilize strong systems engineering skills to ship reliable AI capabilities outside the data center.
Address engineering trade-offs related to compute, memory, energy, and connectivity constraints in production edge AI deployments.