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.