Data Engineer Intern, Marketplace Intelligence & Data - Algo Data (Fall 2026)

As an Algorithm Data Engineer, you will be responsible for the following key areas, transforming data into algorithmic productivity:

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.

• 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.