Graduate Software Engineer - Recommendation Architecture

Responsibilities

Team Introduction： Our Recommendation Architecture Team is responsible for building and optimizing the architecture of the recommendation system to provide the most stable and best experience for users. The team focuses on optimizing the recommendation system architecture, ensuring stability and high availability, and improving the performance of both online services and offline data flows. Collaborating with the algorithm team, we work to enhance recommendation effectiveness and user experience, boost system performance while reducing costs, build data and service mid-platforms, and realize flexible and scalable high-performance storage and computing systems. We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume. Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply. The application limit is applicable to our Company and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early. Responsibilities - Design and optimize recommendation system architectures to improve development efficiency, performance, scalability, and recommendation effectiveness across feeds and vertical content scenarios. - Build and optimize backend systems and services for data security, modularity, computational efficiency, and scalability. - Ensure production system stability through troubleshooting, mechanism design, and tooling development. - Build industry-leading distributed storage and computing systems to support massive data and large-scale business growth. - Drive content understanding architecture innovation, including LLM training/inference optimization and low-latency inference systems. - Collaborate with global teams to tackle architecture challenges and serve TikTok users worldwide.

Qualifications

Minimum Qualifications: - Individuals who are completing or have recently completed a Bachelor's or Master's degree in Artificial Intelligence, Software Development, Computer Science, Computer Engineering or a related discipline. - Strong software programming capabilities, exhibits good code design and coding style; - Familiarity with at least one of the programming languages: Go, Python, Java or C++; - Pragmatic understanding of data structure, algorithm design and analysis, networking, data security, distributed system, and highly scalable systems design. - Familiar with the design of high-concurrency content processing systems, and proficient in distributed frameworks (Flink/Spark) and message queues (Kafka/Pulsar) for content understanding roles. Preferred Qualifications: - Interested in recommendation systems, with an understanding of recommendation principles or execution processes. - Have experience or internship in performance optimization and architecture optimization in high-traffic scenarios. - Agile, quick self learner, highly self-motivated with strong sense of product ownership and creative problem solver. - Have a strong interest in large model training, inference, and optimization, with relevant experience preferred. - Interested in the direction of content understanding and have practical experience in content understanding reasoning or streaming data processing are preferred. - Has experience with large language models (LLMs), including Model Training, model inference, efficiency, and throughput optimization. - Relevant experience in competitions (such as mathematical modeling, programming competitions) is a plus. - Good collaborator and team player, comfortable working in a fast moving, culturally diverse and globally distributed team environment.

Job Information