Intern Assistant Engineer – LLM

Huawei Canada has an immediate internship opening for an Assistant Engineer.

About the team :

The Centre for Software Excellence Lab conducts pioneering research in software engineering, focusing on next-generation technologies. This team integrates industry best practices with cutting-edge academic research to address lifecycle software engineering challenges, including foundation model applications, software performance engineering, hyper-cluster programming, next-gen mobile OS, and cloud-native computing. This lab uniquely allows researchers to apply innovations directly to products affecting billions of customers while promoting open-source contributions, publications, conference participation, and collaborations to create a broader impact.

About the job :

Develop, fine‑tune, and evaluate LLMs aimed at software engineering tasks, such as code generation, bug detection, and test creation using PyTorch and other frameworks.

Implement data preprocessing and training pipelines tailored for code corpora, including tokenization, batching, and dataset management.

Write robust, maintainable code, with tests, documentation, and automated CI / CD integration.

Communicate progress and results, presenting findings in lab meetings and contributing to group knowledge.

Meet top industry and academic leaders and experts around the world, collaborate with top researchers and students, consult with Engineering teams across diverse domains, publish research papers in far-reaching and impactful areas, and submit patent applications for novel inventions.

About the ideal candidate :

Bachelors or Master Degree in Computer Science, Electrical & Computer Engineering, Machine Learning, or relevant domains.

Solid experience with one or more of the following programming languages : Python / C / C++

Familiarity with software development practices (version management, build management, CI / CD, debugging and profiling).

Solid understanding in any of these areas : Machine Learning and / or Deep Learning, Large Models Training and Finetuning (e.g., NLP / CV).

Familiarity with GPU, CPU, or heterogeneous hardware for ML workloads.

Experience with mainstream model training and inference frameworks and tools (e.g., PyTorch, Tensorflow, HuggingFace Transformer&Accelerate, DeepSpeed, Megatron, etc.).

Ability to evaluate, apply, and mature published research to real-world problems on prototype systems and have an inquisitive mindset, proven research and communication.