# Intern Researcher- Audio, Speech & Acoustic AI at Huawei Technologies Canada Co., Ltd.

> Onsite · Canada · Intern · Other · open. Source: https://alderhq.app/jobs/recruitee%3Ahuaweicanada%3A2775350 (read 2026-11-09).

## Facts (observed)

- Company: [Huawei Technologies Canada Co., Ltd.](https://alderhq.app/companies/huawei-technologies-canada-co-ltd)
- Location text: Markham, Ontario, Canada
- Where it can be done from (inferred): Onsite · Canada (evidence: "Location: Markham, Ontario, Canada")
- Posted: 2026-10-13 (27 days ago)
- First seen by alder: 2026-11-07 · last verified: 2026-11-07
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://huaweicanada.recruitee.com/o/intern-researcher-audio-speech-acoustic-ai
- Skills listed (inferred): Python, C++, PyTorch, TensorFlow, LLMs

## Pay and sponsorship

- Posted pay: CAD 58k–CAD 104k per year (from the description)
- Market band for similar posted roles (Canada, n 25): p25 CAD 40k, median CAD 48k, p75 CAD 52k

## Description (the employer's text)

### Huawei Canada has an immediate 12-month internship opening for a Researcher

### About the Role

Huawei Canada CBG team is seeking a highly motivated Audio, Speech, & Acoustics Research Intern to join our AI & Signal Processing Research team. In this role, you will work closely with senior researchers and engineers to explore, design, and implement cutting-edge algorithms at the intersection of acoustic signal processing, deep learning, and speech processing.

### Key Responsibilities

Algorithm Research & Development: Design, prototype, and evaluate novel audio signal processing algorithms and deep learning models for speech and acoustic processing.

Data Pipelines & Dataset Curation: Build robust data pipelines for synthetic and real-world acoustic data augmentation, simulation, and feature extraction (e.g., room impulse response generation, spectrograms, neural representations).

Proof-of-Concept Implementation: Develop efficient clean Python / C++ implementations to test research hypotheses and validate models on benchmark datasets.

Cross-Functional Collaboration: Partner with engineering teams to help translate successful research prototypes into scalable product features.

Publication & Documentation: Clear technical documentation of methodologies and results, with the potential to publish findings at top-tier conferences (e.g., ICASSP, INTERSPEECH, NeurIPS).

The total targeted annual compensation (based on 2,080 hours per year) ranges from $58,000 to $104,000 depending on education, experience, and demonstrated expertise.

### Requirements

### About the Ideal Candidate

Currently pursuing a Master’s or Ph.D. degree in Electrical Engineering, Computer Science, Audio/Acoustic Engineering, or a related quantitative field.

Strong foundations in Digital Signal Processing (DSP) (e.g., STFT, filter design, array processing, room acoustics) and/or Deep Learning for Audio.

Hands-on experience with deep learning frameworks such as PyTorch or TensorFlow.

Strong proficiency in Python (including NumPy, SciPy, Librosa, PyTorch-Audio, or Torchaudio).

Strong analytical skills, curiosity, and passion for solving open research problems in audio.

Track record of research publication in domain conferences (e.g., ICASSP, INTERSPEECH, WASPAA, IEEE TASLP).

Speech AI: Automatic Speech Recognition (ASR), Text-to-Speech (TTS), Voice Conversion, or Speaker Diarization.

Acoustic Signal Processing: Acoustic Echo Cancellation (AEC), Active Noise Control (ANC), Beamforming, Sound Source Localization, or Dereverberation.

Audio Generation & Enhancement: Neural audio codecs, generative audio models (Diffusion/LLMs for audio), or audio source separation.

Spatial Audio: Ambisonics, HRTF modeling, or binaural rendering.

### Additional Information

Huawei Canada is committed to a fair, inclusive, and accessible recruitment process. If you require accommodation during any stage of the hiring process, please let us know and we will work with you to meet your needs. All applications for this position are reviewed directly by our hiring team, we do not use artificial intelligence tools to screen or select candidates.

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/recruitee%3Ahuaweicanada%3A2775350 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.