Senior Engineer - Large Language Model Training

Huawei Canada has an immediate permanent opening for a Senior 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:

• Research and experimentation to enhance reasoning and code generation capabilities in LLMs, with end-to-end ownership from ideation through evaluation to deployment.

• Design and iterate on training pipelines, fine-tuning strategies, and data generation workflows; conduct rigorous analysis to validate improvements.

• Stay current with cutting-edge developments in LLMs, reinforcement learning, and software engineering; apply relevant advances to production-scale systems.

• Author and publish high-impact research papers in leading software engineering conferences and relevant AI/ML venues.

• Collaborate with other Researchers and Engineers to translate research findings into prototypes, tools, or impactful contributions to the field.

• Contribute to the broader research community through activities such as peer review, open-sourcing code/datasets, and mentoring junior researchers (if applicable).

About the ideal candidate:

• PhD/Master in Computer Science, Software Engineering, or a closely related field.

• Demonstrated strong publication record in premier software engineering conferences and journals, specifically on topics related to LLMs for Software Engineering (LLM4SE), or improving the software engineering capabilities of LLMs.

• Publications in top-tier AI/ML conferences with direct applicability to SE is an asset.

• Hands-on experience with deep learning frameworks (e.g., PyTorch, TensorFlow, JAX) and associated MLOps tools, familiary with running experiments on large scale distributed clusters with frameworks like Ray, openRLHF, veRL.

• Deep understanding of Large Language Models, including their architectures (e.g., Transformers), training/fine-tuning techniques (e.g., pre-training, instruction tuning, RLHF), prompting strategies, and evaluation methodologies.

• Proficiency in programming languages commonly used in ML/SE research (e.g., Python).

• Strong analytical, problem-solving, and critical thinking skills, with the ability to conduct independent research.

• Excellent written and verbal communication skills, with the ability to clearly articulate complex technical ideas and research findings.

• A passion for innovation and a drive to make significant research contributions at the intersection of LLMs and Software Engineering.