Skip to content
Huawei Technologies Canada Co., Ltd.

Intern Engineer - AI

Vancouver, British Columbia, CanadaOnsite · Canada
Intern · internship · Graphics Technology Lab
POSTED 22 JUNVERIFIED 18H AGO
Apply Ask
Role
138 days opentrue age
Posted 22 Jun
Payin text
CAD 58k–104kposted
▲ +22% vs market median CAD 66k
CAD 58kCAD 104k
Market p25–p75
CAD 66k
Vancouver · n=9 · 365d to 6 Nov
CompanyHuawei Technologies Canada Co., Ltd. →
187open roles
Opened · 30d
+0
Closed · 30d
−0
30d window
Market
1,692open · Intern Software engineering
All software engineering
75,011open
All tracked boards · live 8 Nov · n=1,692
PythonPyTorchTensorFlow

Huawei Canada has an immediate 4-month internship opening for an Intern Engineer.

About the team

Established in 2012, the Central Media Technology Institute (CMTI) is Huawei's center for media technology innovation and engineering, enhancing the technical competitiveness of media products. Sitting within CMTI, the Graphics Technology Lab focuses on research and breakthroughs in photography, audio, video, computer graphics, XR, and codec standards to maintain the company's leadership in media technology. Our R&D team comprises skilled CG algorithm experts, researchers, software architects, and standards specialists dedicated to building core capabilities for an improved graphics experience.

About the job

Conduct advanced research and engineering on VFX generation, procedural Shape/Texture/VFX generation to Support R&D.

Transfer the results of the advanced research on AI/CG solutions to solve real-world problems, and land them in various Huawei products.

Provide expert opinions on relevant fields by summarizing research findings in high-quality papers or patents.

The total target 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

Bachelors of Science or higher in computer science or related fields with a focus on computer graphics, computer vision or machine learning.

Backgrounds in machine learning and deep learning, e.g., Symbolic regression, DNN, GAN, ViT, and Diffusion techniques

Experienced on dense estimation including but not limited to procedural generation SVBRDF, super-resolution, inpainting, neural rendering, etc.

Experienced in Python, PyTorch, TensorFlow, etc. and a track record of translating ideas into research prototypes quickly

Source · Huawei Technologies Canada Co., Ltd.First seen 7 NovOriginal ↗