Embedded AI Engineer

Bright Vision Technologies

RemotePosted July 26, 2026

About the role

Bright Vision Technologies is seeking an Embedded AI Engineer for a 100% remote position in the United States. The 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 focuses on model compression, quantization, and hardware-aware optimization, combined with strong systems engineering to deliver AI capabilities outside the data center.

Embedded AI Engineer

Bright Vision Technologies

RemotePosted July 26, 2026

About the role

Bright Vision Technologies is seeking an Embedded AI Engineer for a 100% remote position in the United States. The 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 focuses on model compression, quantization, and hardware-aware optimization, combined with strong systems engineering to deliver AI capabilities outside the data center.

Responsibilities

Design and implement edge AI solutions for diverse hardware including mobile SoCs, NPUs, and embedded accelerators.
Apply quantization, pruning, distillation, and architectural optimization to meet edge constraints.
Optimize latency, energy consumption, and memory footprint on target hardware.
Build cross-platform inference runtimes using TensorFlow Lite, ONNX Runtime, and Core ML.
Tailor models for accelerator backends such as DSPs, NPUs, and mobile GPUs.
Implement on-device model update, versioning, and rollback workflows to enable safe staged rollouts and rapid recovery if issues arise.
Design hybrid edge-cloud architectures that gracefully degrade with connectivity.
Build privacy-respecting telemetry pipelines for continuous improvement.
Collaborate with hardware, firmware, and product teams to align AI capabilities with device constraints.
Ensure secure execution paths, model protection, and integrity verification on edge devices.
Develop benchmarking suites to characterize accuracy, latency, and energy across devices.
Promote responsible A

Responsibilities

Design and implement edge AI solutions for diverse hardware including mobile SoCs, NPUs, and embedded accelerators.
Apply quantization, pruning, distillation, and architectural optimization to meet edge constraints.
Optimize latency, energy consumption, and memory footprint on target hardware.
Build cross-platform inference runtimes using TensorFlow Lite, ONNX Runtime, and Core ML.
Tailor models for accelerator backends such as DSPs, NPUs, and mobile GPUs.
Implement on-device model update, versioning, and rollback workflows to enable safe staged rollouts and rapid recovery if issues arise.
Design hybrid edge-cloud architectures that gracefully degrade with connectivity.
Build privacy-respecting telemetry pipelines for continuous improvement.
Collaborate with hardware, firmware, and product teams to align AI capabilities with device constraints.
Ensure secure execution paths, model protection, and integrity verification on edge devices.
Develop benchmarking suites to characterize accuracy, latency, and energy across devices.
Promote responsible A

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