About The Team

The Shopee Search Team builds large-scale, high-performance retrieval, ranking, and relevance systems that power core user experiences across the Shopee platform. As an intern, you will work alongside senior applied scientists and engineers to design, implement, and evaluate cutting-edge machine learning models that directly impact search relevance, user engagement, and platform GMV.

This role is ideal for candidates passionate about machine learning, large-scale systems, and solving real-world search and recommendation problems using data-driven approaches.

Job Description

I. Machine Learning Research & Development

Develop and experiment with models, including deep learning ranking models, transformer-based architectures, and large-model-enhanced retrieval or reranking methods.
Explore innovative approaches such as generative ranking, multi-task learning, sequence modeling, and vector-based retrieval.
Conduct offline research using Shopee's large-scale datasets and evaluate model improvements in terms of AUC, NDCG, recall@K, and latency.

II. Applied Data Science & Analytics

Perform deep-dive analysis on user search behavior, query intent, click-through patterns, and content features.
Build data pipelines to process and validate large-scale logs using Spark, Hive, or PyArrow.
Conduct A/B test analysis, interpret experiment results, and recommend further improvements.
Identify root causes for search degradation, diagnose model blind spots, and propose data or feature improvements.

III. Production Support

Collaborate with software engineers to deploy models into Shopee's multi-stage search architecture (retrieval → ranking → post-ranking).
Implement efficient inference pipelines and monitor model performance in production.
Optimize models for large-scale production constraints such as latency, memory, and throughput.

Requirements

Strong foundation in machine learning, deep learning, or information retrieval.
Proficiency in Python and experience with ML frameworks such as PyTorch or TensorFlow.
Solid understanding of data structures, algorithms, and linear algebra.
Experience working with large datasets and distributed data processing tools (e.g., Spark).
Ability to independently structure experiments, analyze results, and draw actionable insights.

Preferred Qualifications

Research or practical experience in ranking models, transformers, session-based recommendation, or vector search.
Hands-on experience with ANN libraries (e.g., FAISS, HNSW, ScaNN), graph algorithms (e.g., Swing, SSG, NSG), or generative recommendation systems.
Understanding of large-scale system constraints such as memory-efficient models, quantization, or serving optimization.
Familiarity with SQL, feature engineering pipelines, or search system components (query understanding, intent prediction, content relevance).
Strong communication and collaboration skills; ability to work with cross-functional product and engineering teams.

What You Will Gain

Exposure to real-world search and recommendation system challenges at massive scale.
Opportunities to explore cutting-edge research areas including generative ranking, agent-enhanced search, and multi-modal retrieval.
Experience contributing to high-impact production systems used by millions of users daily.
Mentorship from experienced scientists and engineers in one of Southeast Asia's leading e-commerce companies.
Potential pathways to full-time roles in machine learning, data science, relevance engineering, or applied research.
Potential research papers on applied data science track for top-tier ML or Data conferences.

Machine Learning & Applied Data Science Intern - Search