Machine Learning Researcher - LLM/RAG
Montreal, Quebec, Canada
Huawei Technologies Canada Co., Ltd.
Huawei is a leading global provider of information and communications technology (ICT) infrastructure and smart devices.Huawei Canada has an immediate 12-month contract opening for a Researcher.
About the team:
Founded in 2012, the Noah’s Ark lab has evolved into a prominent research organization with notable achievements in academia and industry. The lab’s mission focuses on advancing artificial intelligence and related fields to benefit the company and society. Driven by impactful, long-term projects, the aim is to enhance state-of-the-art research while integrating innovations into the company's products and services, including LLMs, RL, NLP, computer vision, AI theory, and Autonomous driving.
About the job:
Build the benchmark and testbed for advanced LLMs based on Transformer, Mamba.
Build the testbed for retrieval-augmented generation (RAG) and optimize the efficiency of the database system.
Work closely with the researchers, promptly implement and test the new algorithms and new architectures proposed by the researchers.
Research and develop innovative DL architecture and algorithms for large language models (LLM).
Contribute to the design, implementation, test, and maintenance of research and development frameworks.
Requirements
About the ideal candidate:
Hold a Master or PhD (preferred) in computer science, software or electric engineering or related subjects.
Deep understanding of fundamentals and state-of-the-art techniques in NLP and ML.
Expertise in NLP using machine learning or deep learning.
Strong coding skills mainly in Python, with a focused expertise in PyTorch.
Excellent oral and written communication skills.
You are curious, like to think outside the box and appreciate complex challenges.
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: Architecture Autonomous Driving Computer Science Computer Vision Deep Learning Engineering LLMs Machine Learning NLP PhD Python PyTorch RAG Research
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