Co-op Machine Learning Engineer – AI Research Full-time

Huawei Canada has an immediate 8-month Co-op opening for an Machine Learning Engineer.

About the team:

Founded in 1987, Huawei is a leading global provider of information and communications technology (ICT) infrastructure and smart devices. We have 207,000 employees and operate in over 170 countries and regions, serving more than three billion people around the world. We are committed to bringing digital to every person, home and organization for a fully connected, intelligent world. Huawei continues to invest in basic research and open innovation, and accommodates and addresses customer needs with an open mind, while steering these needs with science and technology. We have also built flexible business models and encourage numerous industries to adopt a vast range of models and applications. Through such initiatives, we are bringing digital to every person, home and organization for a fully connected, intelligent world. The Huawei Edmonton Research Center is founded in 2019 and it focuses on the most cutting-edge technologies in AI, GPU, and software architecture.

About the job:

• Build optimized deep learning architectures in the latest AIGC or LLM domains
• Optimize DL models on various hardware
• Perform Neural Architecture Search(NAS) over various deep learning architectures
• Read academic papers and keep up-to-date with recent advances
• Design, implement and integrate models and algorithms
• Evaluate, analyze and derive insights from experiments
• Closely collaborate with researchers and provide solutions for engineers

What you’ll bring to the team:

• Currently enrolled in a Co-op program in computer engineering or related fields
• Interest in applying latest AI models to computer vision or natural language processing tasks
• Experience in Deep learning frameworks such as PyTorch and TensorFlow
• Excellent communication skills, self-motivated, with creative thinking and attention to details
• Strong software development skills
• Preferred Qualifications:
  – Experience in applying deep learning demonstrated through internships and projects
  – Experience and knowledge with at least one deep learning architecture (Transformers, GNN, CNN, GAN, LSTM, VAE, …)
  – Experience in Bayesian Optimization, Reinforcement Learning, or Evolutionary Algorithms