Search LLM Foundation R&D: Build and optimize the search-domain LLM foundation, integrating search knowledge to rapidly deliver business value; develop and refine the post-training pipeline for search LLMs (ultra-long-text / colloquial-text pre-training, image-text / video multimodal representation, e-commerce product multimodal representation learning, etc.); participate in LLM inference optimization (long-context optimization, model efficiency optimization).
Agent Engineering & Multi-Agent Orchestration (Harness / Loop): Contribute to building the search Agent execution framework (Harness) — unified orchestration of tool calling, planning, memory, and environment interaction; multi-agent cluster scheduling and collaboration algorithms (task allocation, dynamic scheduling, inter-agent communication / alignment / conflict resolution); build the Agent Loop and the "training–inference–evaluation" closed-loop engineering.
Long-term Memory, Self-Improvement & Data Closed Loop (Self-Evolve / RSI): Work on long-term memory mechanisms for LLMs, cross-context knowledge integration, and related directions; engage in cutting-edge exploration of self-evolve / self-improvement and recursive self-improvement (RSI) (automated hyperparameter tuning, training pipeline automation, AI-assisted algorithm design, model iteration closed loop); data synthesis and quality control (high-quality vertical-domain data synthesis, distribution alignment, synthetic data quality evaluation / filtering / refinement).
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Assist business teams in organizing data logic, including basic data and indicator systems.
Analyze data fluctuations and complete business analysis from both business and product perspectives.
Refine data requirements and product optimization suggestions based on feedback from business teams, and promote data implementation and product improvements.
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Campaign & Policy Leadership: Executing marquee programs like the All Star Showcase and CNY Campaigns to incentivize creator participation and market penetration.
Content Excellence: Boosting GMV and traffic efficiency by setting quality standards, managing supply policies, and developing high-quality Live IPs.
Affiliate Growth & Retention: Scaling the "Moving-Sales" creator base through advanced labeling systems, mission-based incentives, and optimized matchmaking tools.
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Education & Foundation: Ph.D. in Computer Science, AI, Mathematics, or a related field, with a strong foundation in data structures, algorithms, and mathematical modeling.
AI/ML Expertise: Solid understanding and research experience in Deep Learning, NLP, CV, Reinforcement Learning, Generative Models, or Multimodal Learning.
Coding & Engineering: Proficient in major programming languages and machine learning frameworks (e.g., PyTorch, TensorFlow), combined with excellent problem-solving, self-learning, and teamwork skills.
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