Researcher – AI Security

The AnShi Lab focuses on developing next-generation gateway software architecture by prioritizing file security and software innovation. The focus is on conducting mid- and long-term research to create high-value patents and guide datacom gateway development. The goal is to establish industry benchmarks through cloud-based, intelligent software engineering. By leveraging local resources and fostering talent, we aim to build a high-performance technical expert team, improving both organizational vitality and personnel capabilities.

•  Partner with malware analysts and security stakeholders to define problem scope, labeling strategy, and evaluation methodology.

• Conduct industry/academic research and produce concise surveys and technical reports.

• Design and implement LLM/GNN/ML approaches for malware/threat classification (e.g., family/behavior classification, malicious vs. benign).

• Build scalable data pipelines for ingestion, preprocessing, deduplication, and dataset management.

• Drive experimentation end-to-end: model training, calibration, benchmarking, error analysis, and iteration.

• Contribute to patents/publications; (Optional) travel/conferences.

The total target annual compensation for this position ranges from $106,000 to $156,000 depending on education, experience and demonstrated expertise.

• Ph.D or Masters degree in Computer Science, Electrical & Computer Engineering, Mathematics, or a closely related field, with a focus on systems, high-performance computing, or databases, or equivalent research experience.

• Very strong foundation in LLM/AI/ML with solid mathematical understanding (Transformers, representation learning, optimization, evaluation).

• Strong programming and engineering skills (Python; deep learning frameworks such as PyTorch/JAX; reproducible experimentation).

• Excellent teamwork and cross-cultural communication; fluent English (written and spoken).

• Malware analysis / reverse engineering experience; cybersecurity analysis background.

• Big data processing experience (Spark/Ray, distributed ETL).

• Knowledge of compilers (e.g., LLVM), operating systems, and low-level programming (C/C++ / embedded C).