软件工程师 – 全球电商搜索基础设施

We're building the next-generation AI search and shopping assistant for TikTok Shop, TikTok's global commerce platform, — powering Q&A cards, in-app chatbot, and visual search experiences that help billions of users discover products, explore stores, and shop through natural conversation.

Our team owns the full search stack: from retrieval and ranking to multi-agent LLM engines, post-training infrastructure, and personalized memory.

We translate cutting-edge research into production systems at global scale, with a focus on relevance, latency, and fast algorithm iteration.

- Build AI Search Agents: Design and ship ReAct-based agents with planning, memory, and tool use;

implement DAG workflows and RAG pipelines for multi-turn shopping assistance and query understanding.

Own the unified Agent Harness across Q&A cards, in-app chatbot, and visual search surfaces — with MCP tool-chain integration and end-to-end A/B support.

- Improve LLM Query Understanding: Drive multi-turn conversation, cross-lingual analysis, and LLM reasoning chains for accurate, trustworthy search results;

optimize answer generation pipelines (quantization, KV cache, continuous batching) for quality and latency.

- Build Personalized Memory Infrastructure: Design high-throughput storage and retrieval for long-term user profiles and real-time memory (MemAgent);

build near-real-time feature pipelines over billion-scale behavior sequences for low-latency serving of personalized signals.

- Contribute to Training and Inference Infrastructure: Collaborate on post-training pipelines (SFT, RL, distillation) and model-serving infrastructure (tensor parallelism, speculative decoding, PD separation) to accelerate experimentation and hit latency targets.

- Ship Research to Production: Bridge research and engineering — partner with algorithm teams to evaluate agent and LLM innovations, accelerate adoption, and ensure new capabilities land stably in production at scale.

- Bachelor’s or Master's in Computer Science, Computer Engineering, or a related technical field.

- At least 3 years of industry experience building large-scale distributed systems, search infrastructure, or low-latency online services.

- Proficiency in C++, Go, or Java (C++ preferred); strong systems fundamentals — data structures, OS, networking, multithreading, and Linux performance tuning.

- Solid grasp of LLM and agent technologies — RAG, tool use, and multi-turn reasoning — with a track record of contributing to production AI systems.

- Excellent system design instincts; able to independently architect and ship reliable, high-performance services; strong communication and ownership.

- Background in large-scale search, recommendation, advertising, or personalization systems — particularly e-commerce search at 100M+ user scale.

- Experience shipping agentic systems or RAG pipelines in production — ReAct, tool calling, DAG orchestration, or MCP integrations.

- Familiarity with LLM inference optimization — quantization, KV cache, speculative decoding, tensor parallelism — or hands-on experience with vLLM, TensorRT-LLM, or SGLang.

- Experience with post-training workflows: SFT, RL (RLHF / PPO / GRPO), distillation, or reward model design.

- Research publications at NeurIPS, ICML, ACL, CVPR, RecSys, or OSDI.