Curriculum Vitae
Algorithm Engineer · CV & LLM
End-to-end experience across CV/LLM training, instruction tuning, RLHF, reward modeling, and production deployment for enterprise AI systems and agents.
About
Algorithm engineer focused on computer vision and large language models. Experienced in detection, segmentation, classification, and tracking, with hands-on work from model training and instruction tuning to RLHF, reward modeling, and production deployment. Led AI agent development end-to-end—policy design, reinforcement learning optimization, and enterprise rollouts. Holder of multiple patents and papers, with strengths in cross-task modeling, complex system engineering, and model capability evaluation.
Education
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Master of Computer Science— · National University in Taiwan (QS 200+)
Led multiple computer vision research projects and transferred outcomes into real-world deployments.
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Bachelor of Computer Science— · National University in Taiwan (QS 200+)
Focused on computer vision research and applied systems.
Work Experience
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Algorithm Engineer · AI Lab, Shanghai (Company name withheld)2022 - Present
Leading CV, LLM, agent, and reinforcement learning systems from core algorithms and training pipelines to evaluation and production deployment. Delivered 10+ vision systems supporting 60+ internal and external scenarios; improved zero-shot vision metrics by 15–40% and boosted inference speed by 30%+. Raised agent workflow success from 40% to 85%+, built a multimodal evaluation suite that tripled regression efficiency, and performed safety tuning across 20+ scenarios and 1,000+ risk patterns, reducing false alarms by 25%.
Projects
Built a unified sandbox integrating 30+ vision models (detection/segmentation/classification) with a single inference interface; supported RTSP/RTMP streams with real-time visualization and pluggable benchmarking; reduced validation cycle from 3 days to hours and increased deployment efficiency by 200%+.
Designed task decomposition, planning, and tool-calling logic with self-correction; built feedback-driven fine-tuning (SFT + PPO/RLOO) for closed-loop optimization; improved success rate from 40% to 85%+, cut average steps by 25%, and reduced workflow generation time by 60%.
Created a fully automated pipeline from task definition to data generation and model training with no manual labeling or pretrained models; reduced labeling cost by 90%+ and achieved 10–25% gains over common zero-shot baselines on target tasks.
Built a multimodal evaluation framework for agent reasoning, cross-image analysis, and structured output consistency; added uncertainty scoring and reward-model feedback, tripling regression speed and improving error localization by 40%+.
Publications & Patents
Authored multiple papers with results transferred to production; details are not publicly disclosed due to privacy.
Multiple patent filings related to cross-task modeling, system architecture, and evaluation; details withheld for confidentiality.
Contact
- Email: h@dmjsz.com
- Web: dmjsz.com