机器学习基础设施工程师实习生 - 2027年夏季

The ads system at TikTok operates on a massive scale and serves millions of advertisers, clients and influencers across the world. The quality of the ads system highly depends on the ability to handle massive data, and machine learning is widely used to improve the quality of our ads. The Ads Infra team is responsible for building highly efficient and stable infrastructure to collect and process data for the usage of machine learning training, serving, and privacy enforcement, including feature engineering, feature store, training data generation, etc. We are looking for talented individuals to join us for an internship. Our internship program offers students hands-on experience, industry exposure, and opportunities to apply their knowledge to real-world challenges while building a strong foundation for personal and professional growth. Interns will gain practical experience, explore potential career paths, and participate in social events, learning programs, and development workshops alongside industry professionals. Candidates may apply to a maximum of two positions across Our Company and its affiliates globally. Applications will be considered in the order they are submitted. Applications are reviewed on a rolling basis, so we encourage you to apply early. Please clearly state your availability in your resume, including your start and end dates. Key Responsibilities: - Develop and optimize advertising recommendation models, covering training, evaluation, inference and user behavior sequence modeling. - Build large-scale data pipelines for sample generation and joining, feature processing, storage and data quality assurance. - Develop ML platforms for distributed training, model distribution, online serving and lifecycle management. - Explore MLLM applications in advertising, including data production, model training, fine-tuning and inference optimization. - Apply AI agents to workflow orchestration, resource scheduling, failure diagnosis and automated operations. - Build machine learning platforms for GPU allocation, scheduling, usage metering, cost accounting and utilization optimization.