QUALIFICATIONS

Minimum Qualifications:1. Bachelor’s or Master’s degree in Computer Science, Electronics, Communication, Artificial Intelligence, or related fields.2. Strong programming skills, proficient in Python, and familiarity with at least one deep learning framework such as PyTorch, TensorFlow, or JAX.3. Academic or project-based experience with AI model deployment and optimization, especially large language models (LLMs), vector databases, RAG techniques, or prompt engineering.4. Basic understanding of distributed systems, cloud-native technologies, and Kubernetes-based infrastructure.5. Excellent analytical and problem-solving skills, with coursework or projects demonstrating the application of data-driven AI solutions.6. Strong communication and teamwork abilities, comfortable collaborating across diverse teams.Preferred Qualifications1. Internship experience or academic projects related to AI solutions within CDN, edge computing, or multi-cloud platforms.2. Familiarity with log analytics platforms (e.g., Prometheus, ClickHouse, ELK) or automated incident management systems.3. Experience with infrastructure-as-code tools (Terraform, OpenAPI) or automation frameworks.4. Coursework or projects in MLOps practices or using deployment tools such as Kubeflow, Ray, or BentoML.

Team DescriptionThe TIGE CDN Platform Dev team provides a highly available, cost-efficient, and top-performing global multi-cloud CDN platform for ByteDance’s internal customers by integrating both self-built and commercial CDNs. The team continuously evolves the platform to include more cloud services beyond CDN, delivering a unified, secure, reliable, and high-performance multi-cloud PaaS solution. We are now expanding our capabilities by integrating AI-driven solutions to significantly enhance platform automation, configuration, and intelligent incident management.Job Description1. Participate in exploring, designing, and developing AI-powered solutions for intelligent log analysis, root cause analysis (RCA), and automated troubleshooting to enhance platform reliability and reduce MTTR.2. Contribute to designing and implementing AI-driven multi-cloud configuration assistants, enabling intuitive and automated interfaces for platform configuration and customer self-service scenarios.3. Work closely with senior engineers, product managers, and operations teams to identify business pain points and apply AI technologies to deliver rapid, tangible improvements.4. Assist in optimizing AI model inference performance and deployment efficiency within a cloud-native, edge computing environment.