AI Engineer Intern

Job Responsibilities:

1. Distributed Training Engineering

• Participate in the implementation of large-scale distributed training solutions.
• Lead the engineering deployment of data parallelism, model parallelism (TP/PP), and ZeRO optimization.
• Continuously tune GPU compute utilization and ensure stability of ultra-large-scale training tasks.

2. Compute Scheduling Optimization

• Deeply involved in the development and optimization of AI task scheduling logic.
• Implement fine-grained resource management, fault self-healing, and efficient Checkpoint mechanisms.
• Solve compute bottlenecks in complex gaming scenarios.

3. End-to-End Model Engineering

• Own the full pipeline from model training to inference deployment.
• Participate in operator performance profiling, model quantization, and high-performance inference pipeline construction.
• Support rapid iteration of AI in gaming business.

4. AI-Driven Engineering Evolution

• Actively adopt AI Coding technologies to improve development efficiency.
• Drive Harness Engineering practices through automated testing and engineering governance.
• Ensure ultimate reliability of underlying infrastructure.

Requirements

Education & Background

• Bachelor's degree or above in Computer Science, Systems Architecture, High-Performance Computing, or related field.

Technical Skills

• Proficiency in at least one of Python, C++, or Go.
• Deep understanding of PyTorch framework.
• Hands-on experience with mainstream distributed training technologies such as DeepSpeed and Megatron-LM.

Foundation Knowledge

• Understanding of distributed systems principles.
• Familiarity with NCCL communication library, RDMA networking, or high-performance storage is a plus.
• Knowledge of containerization infrastructure such as Docker and Kubernetes.

Engineering Experience

• Deep experience with AI Coding tools (e.g., GitHub Copilot, Cursor) is a strong plus.
• Experience with Harness Engineering (engineering governance, automated benchmarking, system stress testing) is a strong plus.

General Qualifications

• Exceptional learning ability with clear logical thinking.
• Ability to efficiently collaborate with the team to solve complex systems engineering problems.
• Strong English technical documentation reading skills.

Nice to Have

• Experience in high-performance backend architecture development.
• Hands-on project experience in LLM training/inference engineering.