ByteDance · Flow
Model Data Platform Product Manager
Supported expert annotation for Seed Code post-training across the full data pipeline: trajectory generation, interactive correction, quality review, and feedback. I contributed to the interactive annotation platform so experts could correct model reasoning, tool calls, and execution results step by step. I also kept the workflow aligned with emerging Agent behavior and used AI-assisted checks to improve both annotation efficiency and data quality. The core idea was to capture expert judgment efficiently and reproducibly as training data that genuinely improves model capability.
My role
Contributed to the interactive trajectory annotation platform, translated algorithm-team toolchain requirements into product capabilities, and kept annotation queues and QA rules aligned with evolving Agent behavior.
Interactive annotation design
- Helped design an interactive trajectory annotation platform around three core elements: Agent reasoning, tool calls, and execution results, with step-by-step expert correction.
- Supported inserting or editing intermediate steps without regenerating every later step, reducing annotation cost.
Agent capability alignment
- Worked with algorithm teams to support emerging patterns including multiple tool calls in one step, internal subagent trajectory annotation, and Plan.
- Updated annotation guidelines and QA rules in parallel so training data stayed aligned with the latest Agent behavior.
AI-assisted quality assurance
- Evaluated machine-QA capabilities and, given their high-precision, low-recall profile, designed a two-path approach: pre-checks during annotation plus AI assistance during quality review.
- Moved issue detection earlier in the workflow; around 40% of issues were resolved during annotation, reducing rework and handling time.
What I learned
Having the model generate first and experts correct its trajectory—rather than feeding it only ideal answers—covers the states the model actually reaches. A data platform makes that expert judgment stable, repeatable, and reusable.
Next experience
ByteDance · TikTok