ByteDance-posted 3 months ago
Los Angeles, CA
5,001-10,000 employees
Publishing Industries

Seed Global Data is a team focused on producing international data for LLMs. For the training of large models, data is the lifeline of model quality - and the Global Data team is working closely with technical, product, and operations teams to ensure effective data production strategies and execution management. As a key member of our LLM Global Data Team, the LLM Training Operations Analyst will play a pivotal role in managing the intricate processes involved in training Large Language Models (LLMs) with diverse datasets. This role focuses on overseeing and improving operational workflows, ensuring that projects are delivered with high quality and efficiency, while leveraging advanced data analysis and prompt engineering capabilities to drive strategic decision making.

  • Design, manage, and optimize workflows for LLM training projects, including training design, QA processes, and performance tracking.
  • Lead multiple projects while ensuring timelines, quality standards, and objectives are met, tracking progress and implementing corrective actions as needed.
  • Collaborate with product managers, engineers, and cross-functional teams to ensure alignment on quality metrics, coordinate global team meetings, and manage external vendor relationships.
  • Apply prompt engineering expertise to optimize model training and evaluation projects.
  • Design and implement prompts that enhance model performance for both technical training tasks and administrative workflow automation.
  • Continuously refine prompting strategies to improve project outcomes and operational efficiency.
  • Lead comprehensive data manipulation and analysis initiatives to support operational decision-making.
  • Manage no-code data platforms like Airtable and Lark Base for data organization.
  • Create dynamic dashboards in sheets and other spreadsheet tools, and manage no-code data tables to conduct rapid analysis that answers critical operational questions.
  • Conduct quality and productivity improvement experiments to enhance operational processes.
  • Lead and support general annotation operation improvement initiatives across various data domains.
  • Develop and maintain guidelines and casebooks to support consistent, high-quality data production.
  • Provide mentorship and guidance to team members, helping to develop their skills and ensuring the delivery of high-quality outputs.
  • Foster a collaborative environment where team members can share knowledge and best practices to improve overall performance.
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