About the Role
The role involves research and development in one (or more) of the projects below, focusing on
applying Large Language Models (LLMs) to improve security engineering and scientific research
tasks.

Responsibilities
• Conduct and apply LLMs (Large Language Models) to design and implement intelligent
technologies for improving various security engineer/scientist research tasks.
• Insight investigation into new technology trends and promote the overall improvement of
Huawei's security engineer/scientist capabilities based on LLMs by introducing industry-leading
technologies, tools, standards, best practices, and independent innovations.
• Collaborate closely with researchers and product teams to help them improve security
engineer/scientist development activities and integrate solutions into the product.
• Write and submit patentable inventions or research papers (at top-tier conferences).

Qualifications
• PhD in Computer Science/security engineer, related Machine Learning field, or equivalent
relevant experience.
• Experience with LLMs building is preferred.

Required Skills
• Proficient in python or C++, deep understanding in common algorithms and data structures.
• Strong publication record (top conferences or journals in the related fields e.g., ICSE, FSE, ASE,
ISSTA, TSE, and TOSEM) demonstrating innovative research.
• Fulfill at least one of the requirements below:
• Solid understanding in AI for security engineer/scientist.
• Experienced in deep learning and machine learning. Familiar with at least one framework (e.g.
pytorch/tensorflow).
• Experienced in advanced security engineer/scientist techniques.

Preferred Skills
• Ability to skillfully apply LLMs related technology to conduct technical research in the field of
security engineer/scientist.
• Ability to use intelligent technology to conduct software code analysis.
• Ability to extract, abstract, express and generalize security knowledge/code knowledge using AI
or large model.
• Ability to search, recommend, complete and generate code (business code and test code) based
on LLMs is preferred.
• Ability to develop code detection, defect detection, defect repair, log generation, etc. based on
LLMs is preferred.
• Candidate who have published research papers.
• A highly self-motivated learner and team player with excellent communication and interpersonal
skills is preferred.