Senior Engineer - Large Language Model Training

Job description

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).

Job requirements

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.