AI Product Manager
Turning model capabilities into measurable, shippable, and scalable AI products.
I understand the principles behind deep learning and large language models, with hands-on experience in model teams and production delivery. I am familiar with data preparation from pre-training through business-side post-training/SFT, and I specialize in building benchmark systems and closing the model-evaluation loop. I work with Context and Harness Engineering to design and tune agents for real business scenarios. With product experience across software, mobile, and smart hardware, I align algorithm, engineering, and business teams to turn model capabilities into product value.
Experience
Internship
Seed
AIGC Model Product Manager
2026.08-Present
Beijing
○ Seedance capability development: Advancing caption-data strategy optimization and building an automated workflow through implementation and launch. Further details are omitted under the applicable confidentiality agreement.
Internship
Global Monetization Product and Technology
AI Advertising Product Manager
2026.06-2026.08
Beijing
○ DM model EQ launch: Contributed to SFT for Qwen3.5-397B, raising its EQ score from 50.07 to 64.37. Combined with lead-capture frequency optimization, the launch delivered a statistically significant +4.54% lift in CVR2.
○ DM model v2.0 iteration: At the foundation-model layer, identified Qwen3.5-397B's high cost, lack of audio/video capabilities, and latency constraints; helped restart the foundation-model and workflow discussion and define evaluation dimensions covering DM capability, cost, latency, multilingual support, and multimodality. At the evaluation layer, diagnosed overlapping v1.0 EQ rubrics and an LLM judge that was not aligned with human review; contributed to rebuilding Golden Data and the rubric system from scratch, stratifying SFT training data and benchmark distributions by user intent × vertical, and introducing an iterative human-model alignment workflow. The first Golden Data version has been completed.
○ Agent flow optimization: Contributed to launching a dynamic knowledge base and memory mechanism for merchants' personalized bot-reply styles. The system summarizes business knowledge and messaging preferences on a T+1 basis from advertiser webpages and real conversations, addressing the stateless nature of LLMs. Led SBS human evaluation, which showed materially stronger cross-session coherence and personalization than the baseline, and advanced the solution into an A/B test.
○ Machine-evaluation interception: Contributed to an S0-S3 bad-case severity framework and rolled out full machine evaluation for S0/S1 cases, reducing latency from 16 seconds to 5 seconds while meeting safety requirements.
○ User journey optimization: Identified lead loss outside business hours and launched a configurable night-shift mode to take over high-potential nighttime leads and recover otherwise lost customers.
Projects
Education
Education
Master of Computing
2024.08-2027.06
GPA: 4.0/5.0
Main courses: Machine Learning, Neural Networks and Deep Learning, Reinforcement Learning, Probability and Mathematical Statistics, Advanced Software Development.
