Senior Audio Algorithm Expert

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

On behalf of Huawei, a world-renowned information and communication technology company, we are seeking passionate and talented individuals to join our team as Senior Audio Algorithm Expert.

Role Overview

We are seeking a Senior Audio Algorithm Expert to lead the design and development of low-power, low-latency, real-time audio algorithms for edge-side and embedded platforms. This role focuses on smart terminals such as TWS earphones, laptops, and IoT devices, bridging advanced audio algorithms with constrained hardware systems at scale.

Core Responsibilities

1. Edge-Side Audio Algorithm Architecture

• Lead the architecture design and implementation of low-power, low-latency audio algorithms for embedded and edge devices.
• Take technical ownership in one or more of the following domains:
• Speech Enhancement: noise suppression, echo cancellation, multi-microphone array processing
• Wearable Audio Systems: Active Noise Control (ANC), transparency mode, hearing enhancement, motion-aware audio features
• Intelligent Voice Interaction: ASR front-end processing, TTS enhancement, voice separation, speaker recognition
• Micro-Acoustic Modeling: nonlinear compensation for miniature speakers and microphones

2. Embedded Optimization & Deployment

• Deploy and optimize audio algorithms on resource-constrained platforms (DSP / NPU / ARM Cortex-M).
• Design collaborative architectures across algorithms, hardware, and firmware, optimizing power consumption, latency, and memory usage in real-time systems (RTOS / embedded Linux).
• Lead edge-side AI model optimization, including quantization, compression, and acceleration (e.g., INT8 / FP16, pruning), ensuring production-level performance and stability.

3. Cross-Functional & System-Level Collaboration

• Work closely with hardware and acoustic teams on system design, including MEMS microphone selection, acoustic tuning, and speaker system optimization.
• Collaborate with platform and connectivity teams to optimize audio performance over Bluetooth LE Audio and Wi-Fi in end-to-end products.
• Provide technical guidance and mentorship to senior and junior engineers within the audio algorithm team.

4. Technical Leadership & Innovation

• Track and evaluate state-of-the-art audio research (e.g., ICASSP, INTERSPEECH, IEEE TASLP) and translate research outcomes into practical, scalable product solutions.
• Define mid- to long-term technical roadmaps for edge-side audio systems (e.g., hybrid AI + DSP architectures).

Qualifications

Required Technical Background

• PhD (or exceptional Master’s degree) in Computer Science, Signal Processing, Electrical Engineering, Acoustics, or a related field.
• Extensive hands-on experience in edge-side / embedded audio algorithm development, with a proven record of delivering production-ready systems.
• Strong expertise in audio signal processing, including:
• Fourier analysis, subband processing, active noise control, hearing-aid algorithms
• Adaptive filtering (LMS / NLMS), psychoacoustic modeling
• Solid experience with embedded platforms and real-time systems:
• RTOS (e.g., FreeRTOS, Zephyr) and/or embedded Linux
• DSP architectures (e.g., Cadence / Tensilica or similar)
• Proficiency in C/C++ and Python and/or MATLAB, with the ability to independently develop, debug, and optimize edge-side algorithms.
• Familiarity with deep learning frameworks (PyTorch / TensorFlow / ONNX) and experience deploying models on edge devices.
• Demonstrated mass-production experience in consumer audio products (e.g., ANC algorithms for TWS earphones).

Industry & Domain Knowledge

• Solid understanding of audio-related standards and ecosystems (e.g., ITU-T, AES, MPEG).
• Experience working with mainstream audio chip platforms (e.g., Qualcomm/CSR, TI, MediaTek) or FPGA-based prototyping flows.
• Strong sensitivity to audio quality, balancing subjective listening evaluation with objective metrics (SNR, PESQ, THD+N, etc.).

Preferred Qualifications (Bonus Points)

• End-to-end ownership experience in audio algorithm design for edge-side SoC platforms (e.g., BES or similar).
• Granted patents or significant open-source contributions (e.g., Kaldi, WebRTC, SPTK).
• Strong performance in international audio challenges (e.g., DCASE, MLSP).
• Multilingual speech processing experience (Mandarin, English, dialects).
• Experience with algorithm–hardware co-design, including optimization for dedicated audio accelerators.