About the Team
The security engineering team is missioned to build security services, platforms and technologies, as well as to support cross-functional teams to protect our users, products and infrastructures. In this team you'll have a unique opportunity to have first-hand exposure to the strategy of the company in key security initiatives, especially in building scalable and secure-by-design systems and solutions. You also have opportunities to go through the whole lifecycle of security products or services, are encouraged to participate in each phase, each part of the projects and have the whole picture of what we are working on.
As a project intern, you will have the opportunity to engage in impactful short-term projects that provide you with a glimpse of professional real-world experience. You will gain practical skills through on-the-job learning in a fast-paced work environment and develop a deeper understanding of your career interests.
Applications will be reviewed on a rolling basis - we encourage you to apply early.
Successful candidates must be able to commit to at least 3 months long internship period.
Responsibilities:
- Improving the intelligent upper limit of large models in the security field.
- Training vertical large models and applying them to security tasks, such as code analysis, vulnerability detection, attack research and judgment, threat intelligence, SDLC (Secure Development Lifecycle)
- Deeply applying and innovating large model technology stacks, such as data synthesis, model post-training (SFT/GRPO/Agent), model acceleration and distillation, exploring technical boundaries.
Minimum Qualifications
- Undergraduate, or Postgraduate who is currently pursuing a degree/master in Computer Science, Computer Engineering, Security, Statistics, Artificial Intelligence, or other STEM disciplines.
- Proven experience with C++/Java/Python, with excellent coding ability, and at least familiar with one common machine learning/deep learning platform
- Proven skills with common algorithms and theories of data mining, including data denoising/statistical learning/classification and clustering/anomaly detection/graph learning and other algorithms.
- Proven experience of relevant experience in big data processing, proficient in using Hive, SQL, Spark, Flink for big data analysis and mining.
Preferred Qualifications
- Have the ability to implement machine learning algorithms, and those with relevant practical experience are preferred.
- Enthusiastic about challenging things, with self-drive, fast learning ability, good communication skills and innovative thinking.
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