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.

Requirements

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.