Software Engineer – Global E-Commerce Search Infrastructure (TikTok Shop)

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. Responsibilities: - 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. Minimum Qualification(s): - 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. Preferred Qualification(s): - 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.