Job Description:

In a data-driven and evaluation-driven manner, build an efficient closed loop for data iteration and establish an end-to-end data system spanning data sourcing, labeling, processing, synthesis, and evaluation. Continuously build high-quality datasets and evaluation sets to keep improving foundation model capabilities and to drive the development of AI models and applications.

Responsibilities cover one or more of the following directions:

Design and implement high-performance, scalable, and distributed data infrastructure covering the full lifecycle - data storage, ingestion, cleaning, labeling, management, and analysis - and continuously improve data engineering efficiency.
Design audio-visual multimodal training data strategies develop efficient data processing, synthesis, and optimization operators and pipelines, and build a multimodal data asset repository to meet the data needs of large model development.
Build a data-model-evaluation closed loop together with Agents, using data to drive rapid iteration of large models.
Track cutting-edge techniques and methods in the large-model data domain, explore innovative approaches such as data augmentation, data distillation, and high-quality data filtering, and land them in real business scenarios to increase data value.

Requirements:

Bachelor's degree or above in Computer Science, Software Engineering, Data Science, Statistics, or a related field.
Solid programming fundamentals proficient in Python and Java, competent in SQL strong command of common data structures and algorithms.
Familiar with at least one big data processing framework (any of Spark / Flink / Ray), or strong self-learning ability backed by relevant coursework / projects.
Familiar with the fundamentals of large models / multimodal / AIGC (LLM, Diffusion, T2V, CLIP / VLM, etc.), or having relevant side projects.
Familiar with the fundamentals of Agents (Tool Use, trajectory, Memory, multi-turn interaction), or having worked on Agent-related projects.
Good data sense: able to identify data quality issues and willing to be rigorous about data accuracy.
Good communication skills and a collaborative mindset.

Good To Have:

Experience with large-scale data processing (from coursework / internships / competitions), having handled TB-PB scale data. Familiarity with data warehouse dimensional modeling (Kimball / OneData approach).
Experience with model evaluation / benchmarking (exposure to VBench, ELO, GSB, Langfuse, etc. is a plus).

AI Data Engineer , Marketplace Intelligence & Data (2027 Graduate)