AI Infrastructure Intern

What the Role Entails - Design and implement high-performance orchestration systems to automate the deployment, scaling, and lifecycle management of large-scale model training and real-time inference services - Develop scheduling algorithms to optimize the utilization of global GPU/CPU clusters. Focus on improving resource allocation efficiency and managing the complexities of heterogeneous hardware - Participate in development of training-related requirements (incremental training and API for online learning) and enhance framework efficiency - Participate in development of pipelines for offline-to-online synchronization. Ensure strict data consistency and minimize model-update latency to ensure the most current intelligence is served - Collaborate with cross-functional teams to ensure platform features are available, secure, and compliant for a global user base. Who We Look For - Currently enrolled in an undergraduate or graduate degree program in Computer Science, Information Systems, or related fields - Experience in machine learning system practice and open-source ML orchestration frameworks (e.g. Ray/TFX/Kubeflow) - Proficiency in backend software design, development, and deployment practices with at least one of the following programming languages: Golang, Python, C++, or Java - In-depth knowledge of distributed system principles (e.g., consistency protocols like Paxos or Raft, distributed locking, and caching strategies) - Familiarity with Linux operating system and common system tools - Strong ownership, customer-oriented values, and integrity demonstrated - Good programming discipline, fast-learning ability, and teamwork skills - Prior internet industry work or internship experience is a plus - Fluency in both English and Mandarin Chinese for effective communication with international stakeholders