Responsibilities
Team Introduction: TikTok is an international short video platform covering 150 countries and regions. We hope to discover real and interesting moments through TikTok and make life better. TikTok has offices around the world, with global headquarters in Los Angeles and Singapore, and offices in New York, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul and Tokyo. TikTok AI Innovation Center is a department dedicated to AI infrastructure construction and innovative research, exploring industry-leading artificial intelligence technologies, including large language models, multimodal large models and other research directions. We hope to develop model algorithms that can handle multi-language and massive video content understanding to bring users a better content consumption experience. In the Code AI direction, we use the powerful code understanding and reasoning capabilities of large language models to improve program performance and R&D efficiency. 1. Participate in the design and implementation of a highly available, scalable, and distributed large model machine learning platform to support the development and efficient iteration of international short video large models 2. Explore the industry's cutting-edge large model engineering R&D (LLMOps) related technologies, covering data, training, reasoning services, evaluation, automated orchestration, prompt engineering, resource scheduling and other directions 3. Build a high-performance large model reasoning service architecture to reduce reasoning costs and ensure high-availability operation of services.
Qualifications
1. Bachelor degree or above in computer science or related majors, good communication and teamwork skills 2. Solid programming foundation, good programming style, familiar with multi-threaded programming, distributed computing, network communication, memory management, design patterns 3. Two years or more of engineering R&D or infrastructure experience, proficient in at least one development language such as C/C++, Python, Golang 4. Experience in R&D of distributed systems, ability and experience in optimizing system performance issues, those who meet one or more of the following conditions are preferred: 1) Experience in R&D of ultra-large-scale models, involving data, training, deployment, evaluation and other related work 2) Experience in DevOps and MLOps 3) Experience in deployment and optimization of large-scale model services 4) Familiar with deep learning frameworks such as TensorFlow, PyTorch, PaddlePaddle, and understanding of large model engineering frameworks such as vLLM/LangChain 5) Experience in distributed scheduling, computing and storage projects such as K8s, Ray, Hadoop, Spark, HDFS, etc.
