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.
