Team Introduction TT-Search Algorithm & Applied AI is the algorithm team behind the search business built on TikTok (TikTok Search), with the goal of becoming the search engine of choice for users worldwide. Compared with recommendation systems, which passively infer user intent, search delivers content based on users' discovery motivation — making intent expression far more precise. This search data can also feed back into the recommendation engine, helping users obtain more relevant content. We are at an inflection point where the search paradigm is shifting from "retrieval-and-ranking" toward "Agents proactively completing tasks." With large language models and Agents as our two driving wheels, the team builds the search-domain LLM foundation, the Agent execution framework (Harness), multi-agent collaboration, and self-improvement closed loops. We support the implementation of business scenarios such as multimodal AIGC creation, visual search, on-device intelligence, and long-horizon task Agents — spanning POI search, Wish search, automated evaluation, infrastructure, and more.
We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume.
Job Responsibilities
- 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).
- Evaluation & Reward/Verifier Systems: Help build online Reward/Verifier systems and automated evaluation frameworks — annotation-free automated evaluation, evaluation of long-cycle complex tasks and cross-domain capabilities, and multi-agent collaboration evaluation standards.
- Business Implementation: Long-horizon task Agents (persistent intent) and multimodal AIGC creation: leveraging SOTA models for image/video generation to power the Feed's "ask-after-viewing / create-after-viewing" experiences and strengthen users' proactive mindset; and visual search & on-device intelligence: object detection, OCR, TinyLLM; and search content / creator ecosystem, etc.
Minimum Qualifications:
- Individuals who are completing or have recently completed a PhD degree in computer science or a related discipline.
- Solid foundation in machine learning / deep learning and familiarity with cutting-edge LLM and Agent technologies; publications at top-tier conferences such as NeurIPS / ICML / ICLR / ACL / EMNLP / CVPR / ICCV / ECCV / AAAI, or competition awards, are a plus.
- Experience with LLM / Agent-related projects is preferred: LLM pre-training / fine-tuning / alignment, Agent development (tool calling, planning, memory, multi-agent collaboration), RAG, RLHF / RLAIF, data synthesis, automated evaluation, etc.
- Familiarity with PyTorch / TensorFlow for model training and deployment; understanding of acceleration methods such as distributed training and mixed-precision training; knowledge of model compression and inference acceleration (quantization, pruning, distillation, TensorRT, etc.).
- Familiarity with big data frameworks and applications (MapReduce / Spark, etc.) is a plus.
Preferred Qualifications:
- Familiarity with any of the following directions is preferred:
- LLM & Agent: pre-training / SFT / alignment, long context, Agent Harness and multi-agent orchestration, data synthesis, self-improvement (Loop / RSI), automated evaluation;
- CV & Multimodal: image / video retrieval, classification and recognition, image segmentation, object detection, OCR, graph neural networks, multimodal learning, self-/unsupervised learning; experience with CV / multimodal large-model projects, or awards in Kaggle / COCO / ImageNet / ActivityNet, are a plus; CVPR / ICCV / ECCV publications preferred;
- NLP: pre-training, natural language understanding, multilingual / cross-lingual learning, natural language generation, transfer / semi-supervised learning; LLM project experience, or awards in GLUE / SuperGLUE / CLUE, are a plus; ACL / EMNLP publications preferred.
- Excellent engineering implementation and learning ability, strong collaboration and communication skills, and genuine passion for search and Agent directions.