Beschreibung
About the role Datago turns Chinese and English financial text into structured data products: sentiment scores, named-entity recognition, event classification, used by institutional clients. The team fine-tunes BERT-family encoders on proprietary corpora, maintains rigorous evaluation standards, and builds pipelines around them. As the models are delivered to paying clients, evaluation standards are strict and all results must be auditable.
This is a model development role rather than a prompt-engineering position. The work spans training loops, dataset preparation and evaluation design, and the engineer is accountable for the figures the models produce. AI assistants are in the workflow but responsibility for results remains with the engineer.
Key responsibilities
Fine-tune and maintain encoder models (BERT, and more) for classification, NER and sentiment tasks. Design evaluation frameworks that reflect client priorities: ranking consistency, correlation against ground truth, and strict point-in-time splits. Manage data annotation workflows end to end, including guidelines and quality control.
Curate large text corpora, covering sampling, cleaning, deduplication and versioning. Optimize model serving where required, via ONNX export, quantization and batch inference. Maintain experiment records in W&B or an equivalent tool, logging each run's configuration, metrics and artifacts so results can be reproduced and compared.
Maintain bleeding-edge knowledge in relating areas. Abo
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