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TikTok has filed one layoff notice in the last twelve months, covering 250 workers, most recently August 2026. These are public filings with state labor departments, and an open role can sit alongside them. See the filings

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About the job

About the Team: 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 Qualifications: - Bachelor’s or Master's in Computer Science, Computer Engineering, or a related technical field. - At least 5 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 Qualifications: - 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.