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:
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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.
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Implement data preprocessing and training pipelines tailored for code
corpora, including tokenization, batching, and dataset management.
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Write robust, maintainable code, with tests, documentation, and automated
CI/CD integration.
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Communicate progress and results, presenting findings in lab meetings and
contributing to group knowledge.
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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:
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Bachelors or Master Degree in Computer Science, Electrical & Computer
Engineering, Machine Learning, or relevant domains.
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Solid experience with one or more of the following programming languages:
Python/C/C++
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Familiarity with software development practices (version management, build
management, CI/CD, debugging and profiling).
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Solid understanding in any of these areas: Machine Learning and/or Deep
Learning, Large Models Training and Finetuning (e.g., NLP/CV).
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Familiarity with GPU, CPU, or heterogeneous hardware for ML workloads.
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Experience with mainstream model training and inference frameworks and tools
(e.g., PyTorch, Tensorflow, HuggingFace Transformer&Accelerate, DeepSpeed,
Megatron, etc.).
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Ability to evaluate, apply, and mature published research to real-world
problems on prototype systems and have an inquisitive mindset, proven
research and communication.
