You will design, build, and optimize the core infrastructure that supports TikTok Shop’s recall, ranking, and re-ranking pipelines globally. - Core Search Engine Development: Design and implement high-performance online retrieval systems. Optimize core components including the inverted index, vector retrieval (ANN/ HNSW), query understanding, and merger logic. - Real-Time Data Pipelines: Build highly scalable and fault-tolerant data pipelines using Flink, Kafka, and Spark to ensure product changes (price, stock, and new listings) are reflected in search results in near real-time. - System Stability & Performance: Drive latency, throughput, and cost optimizations for services handling hundreds of thousands of QPS. Troubleshoot complex distributed system issues, manage cross-region failover, and design high-availability disaster recovery solutions. - Large-Scale Storage & Retrieval: Design and optimize distributed storage libraries (based on Rocks. DB/ Redis) and columnar databases tailored for high-speed e-commerce feature retrieval. - ML Infrastructure Collaboration: Work closely with Algorithm/ ML Engineers to productionize state-of-the-art Large Language Models (LL - Ms) , AI Search, and multi-modal search models, ensuring the infrastructure supports massive model serving and real-time feature engineering. Requirements: Minimum Qualifications: - Bachelor’s in Computer Science, Computer Engineering, or a related technical field. - At least 3 years of hands-on experience building large-scale distributed systems, search engines, or low-latency online services. - Strong coding proficiency in C , Go, or Java (C / Go is heavily preferred for core infra roles). - Deep understanding of computer science fundamentals: Data structures, algorithms, operating systems, network programming, and multi-threading. - Experience with distributed system technologies, such as RPC frameworks (g. RPC/ Thrift), Message Queues (Kafka), and Stream Processing (Flink/ Spark). Preferred Qualifications: - Search Internals: Direct experience with search engine internals (e.g., Lucene, Elasticsearch, Solr, Vespa) or building custom inverted index/retrieval systems. - Vector Search: Experience with vector database technologies or libraries (FAISS, HNSW, Sca. NN). - Storage Engines: Deep understanding of NoSQL and KV stores (Redis, Rocks. DB, H - Base) or columnar storage systems. - Performance Optimization: Proven track record of reducing p 99 latency, optimizing memory usage in C , or driving measurable efficiency gains in high-traffic systems. - Domain Knowledge: Experience working in E-commerce, Ad. Tech, or large-scale consumer platforms.