Vacancy Description
Responsibilities
- 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.
- A...
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