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.