Lead AI Engineer

Build and maintain end-to-end training data pipelines for Large Language Models (LLMs), covering data cleaning, deduplication, format conversion, quality filtering, data mixture/formulation, and version management.
Design and dynamically adjust the data mix ratio between SFT training data and general-purpose Replay data.
Manage the Prompt pool for distillation training, including prompt collection, deduplication, and balanced sampling across different domains.
Establish a comprehensive data quality assurance framework, implementing a two-level automated and manual sampling-based quality control process covering format validation, semantic deduplication, and persona consistency checks.
Collaborate with Agent Algorithm Engineers to convert ReAct multi-turn reasoning trajectories and Function Calling workflows into training-ready data formats.
Manage training data versioning and traceability, ensuring that data snapshots and change records for each training iteration are fully documented and traceable.

Bachelor’s degree or above in Computer Science, Artificial Intelligence, Data Science, or a related field.
Proficient in Python and familiar with data processing frameworks such as pandas, with hands-on experience processing large-scale text datasets.
Experience building LLM SFT/RLHF data pipelines, with a solid understanding of best practices in data deduplication, quality filtering, and data mixture/formulation.
Familiarity with ChatML/Chat formats, Function Calling data formats, and multi-turn conversational data structures.
Experience in data quality measurement, monitoring, and data version management.

See also