Data Engineer Intern, Marketplace Intelligence & Data - Algo Data (Fall 2026)
Posted on June 18, 2026 by Shopee
- Singapore, Singapore
- N/A
- Internship
Department Engineering and Technology
LevelInternship
LocationSingapore
The Engineering and Technology team is at the core of the Shopee platform development. The team is made up of a group of passionate engineers from all over the world, striving to build the best systems with the most suitable technologies. Our engineers do not merely solve problems at hand; We build foundations for a long-lasting future. We don't limit ourselves on what we can or can't do; we take matters into our own hands even if it means drilling down to the bottom layer of the computing platform. Shopee's hyper-growing business scale has transformed most "innocent" problems into huge technical challenges, and there is no better place to experience it first-hand if you love technologies as much as we do.
About the Team:
The mission of the Marketplace Intelligence and Data team is to build sustainable, efficient data and intelligence products that power Shopee’s business growth. The team is responsible for Shopee’s e-commerce data warehouse, merchant and operations data products, end-to-end traffic data, product algorithms (including product listing, governance, content optimization, SPU cataloging and price comparison), marketing algorithms (including merchant onboarding, assortment, and recommendations), review algorithms, user profiling, as well as foundational AI capabilities such as machine translation, speech processing, computer vision, and identity verification.
As the core horizontal data team supporting all algorithm teams within Marketplace Intelligence and Data, the Algo Data Team aims to be the most reliable strategic data partner for algorithm development. We are committed to delivering efficient, stable, and high-quality data services that accelerate algorithm iteration and transform data into strong commercial value for Shopee.
Job Description:
As an Algorithm Data Engineer, you will be responsible for the following key areas, transforming data into algorithmic productivity:
Feature Platform & Feature Store Construction:
Feature Platform & Feature Store Construction:
- Lead or participate in the design, development, and maintenance of enterprise-level feature platforms / feature stores for both traditional models and LLMs. Address challenges such as online-offline feature consistency, real-time performance, and availability. Standardize and automate feature engineering pipelines to improve the efficiency of algorithm teams.
High-Quality Dataset Construction and Maintenance:
- Design and build high-performance, low-latency offline and real-time datasets for model training, evaluation, and online inference scenarios. This includes pre-training dataset construction, data filtering, data quality evaluation, data augmentation, and automated evaluation pipelines.
Algorithm Experimentation and Monitoring Pipelines:
- Participate in building and maintaining the core data pipelines for algorithm experiments, providing end-to-end support from data preparation, configuration, and execution monitoring to metric analysis and result interpretation.
High-Value Label and Knowledge Graph Mining:
- Leverage deep understanding of e-commerce business and algorithms to mine high-value user profiles, item labels, and relationship graphs from massive behavioral data, effectively feeding back into model optimization and business strategy.
Requirements:
- Currently pursuing a Bachelor's degree (or higher) in computer science , Artificial Intelligence or related fields.
- Familiar with one or more big data technologies such as Spark, Flink, Hadoop, HBase, Kafka, Druid, ClickHouse.
- Excellent logical thinking, communication, project management, and cross-team coordination skills.
- Highly self-motivated, resilient under pressure, and eager to continuously explore and drive business breakthroughs.
Preferred Qualifications
- Experience with LLM pre-training data pipelines, Data Lake, Data Flywheel, or vLLM.
- Background in model evaluation (benchmarks) and model training (pre-training) is a strong plus.
Advertised until:
July 18, 2026
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