Gen-AI Research Scientist

7 days ago

Toronto, Ontario, Canada Huawei Canada Full time

About Huawei Canada:

Huawei Canada has an immediate 12-month contract opening for an Engineer to work on cutting-edge Gen-AI projects.

The Distributed Scheduling and Data Engine Lab is a technical innovation center focused on researching and developing advanced cloud technologies. Established in 2014, the lab supports productization and iterative optimization of its technical achievements.

As a member of our team, you will design and implement Gen-AI features using LLMs, collaborating with UX designers and front-end developers to incorporate UI design into the Gen-AI platform.

About the Job Requirements:

  • Design and implement Gen-AI features using LLMs, customizing them through techniques such as prompt engineering, RAG, and fine-tuning.
  • Collaborate with UX designer and front-end developer to incorporate UI design into the Gen-AI platform, working closely with platform software engineers for integrating Gen-AI features into existing solutions.
  • Conduct technical research, present findings to the team, and develop AI-driven products using multimodality, leveraging expertise in cutting-edge Gen-AI methodologies.
  • Work closely with industry experts to create a highly competitive cloud platform, focusing on current research areas including cloud native databases and infrastructure resource scheduling and prediction.

About the Ideal Candidate:

  • Strong background in mathematics and statistics & strong foundation in algorithms, data structure, and object-oriented programming along with proficiency in Python, R, and Java.
  • Experience in using ML and exploratory data analysis (EDA) tools and libraries including Numpy, Pandas, Matplotlib, Seaborn, and Scikit-learn.
  • In-depth knowledge of Artificial neural networks like CNN, RNN, LSTM, and GRU, along with expertise in DL frameworks such as PyTorch, TensorFlow, and JAX.
  • Deep theoretical knowledge of advanced NLP and Gen AI concepts including transformers, pre-training and transfer learning, along with experience in fine-tuning Auto-encoder, Autoregressive and Seq2Seq Language Models from Hugging Face.
  • Expertise in classical and modern Reinforcement Learning techniques, including natural language-based RL, RLHF, and preference alignment.

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