Senior Edge Audio Architect

Beijing Foreign Enterprise Management Consultants Co.,Ltd.; Singapore; On-site; SGD 140,000 - 210,000; Full time

Job summary

Huawei is seeking a Senior Audio Algorithm Expert to lead the design of low-power, low-latency audio algorithms for edge devices such as TWS earphones, laptops, and IoT products. You will own architecture, optimization, and cross-functional collaboration across hardware and firmware teams.

Ideal candidates have PhD or exceptional Master’s degrees with hands-on experience in production-ready edge audio systems, DSPs, RTOS/embedded Linux, and deep learning deployment on edge devices.

Qualifications

• PhD or exceptional Master’s degree in Computer Science, Signal Processing, Electrical Engineering, Acoustics, or related field.
• Extensive hands-on experience in edge/embedded audio algorithm development with production-ready results.
• Proficiency in audio signal processing and edge deployment including DSP, RTOS, and embedded Linux.

Responsibilities

• Lead architecture design of low-power, low-latency audio algorithms for embedded/edge devices.
• Own one or more domains: Speech Enhancement, Wearable Audio Systems, Intelligent Voice Interaction, or Micro-Acoustic Modeling.
• Deploy and optimize algorithms on DSP/ARM Cortex-M with RTOS/embedded Linux.
• Collaborate with hardware/firmware teams, drive system-level optimization for power, latency, and memory.

Skills

C/C++

DSP

Python

MATLAB

RTOS / Embedded Linux

Deep Learning (PyTorch / TensorFlow)

Edge/Embedded audio

Education

PhD or exceptional Master’s degree in CS/Signal Processing/EE

Strong hands-on edge audio experience

Tools

Cadence DSP

Tensilica DSP

FreeRTOS

Zephyr

Embedded Linux

ONNX

Job description

Huawei is seeking a Senior Audio Algorithm Expert to lead the design of low-power, low-latency audio algorithms for edge devices such as TWS earphones, laptops, and IoT products. You will own architecture, optimization, and cross-functional collaboration across hardware and firmware teams.

Ideal candidates have PhD or exceptional Master’s degrees with hands-on experience in production-ready edge audio systems, DSPs, RTOS/embedded Linux, and deep learning deployment on edge devices.