Algorithm Engineer Intern, Search NLP (Aug-Dec 2025)

Descripción del puesto:

* Research and develop LLM applications in e-commerce search, including AI-powered search, relevance modeling, LLM-based retrieval, and multimodal solutions.
* Assist in algorithm experiments and performance evaluations to validate LLM effectiveness in real-world search scenarios.
* Learn and contribute to LLM optimization and deployment, including inference optimization and model distillation.
* Stay updated on the latest research in NLP and LLMs, and actively contribute to team discussions and innovations

Requerimientos del candidato/a:

* Bachelor's degree or above in Computer Science, Machine Learning, Statistics, Pattern Recognition, or related fields.
* Able to commit at least 3 months.
* Strong foundation in machine learning and NLP (e.g., Transformer, BERT).
* Proficiency in Python and familiarity with at least one deep learning framework (PyTorch/TensorFlow).
* Hands-on experience in model training or experimentation.
* Strong interest in LLMs and search-related applications, fast learner, and independent thinker.
* Preferred (Nice-to-Have): Achievements in AI/NLP competitions (e.g., Kaggle, Tianchi), or experience in research publications or open-source projects.

We are looking for highly motivated NLP Algorithm Interns to join the Shopee Search team. You will have the opportunity to explore the integration of Large Language Models (LLMs) with search algorithms, working on cutting-edge topics such as AI-powered search, query understanding, semantic and multimodal retrieval, and search relevance optimization. Guided by experienced mentors, you will apply research outcomes to real-world e-commerce search scenarios serving hundreds of millions of users.

This is a fast-growing opportunity to learn how state-of-the-art NLP and LLM technologies can solve real business problems and potentially deliver high-impact results during your internship.

We welcome passionate, self-driven individuals to join us in innovating and shaping the future of global search