Research MLE (Training Optimization) 大模型训练优化工程师

📋 Description

• Design, implement, and optimize large-scale machine learning systems for training
• Improve all aspects of performance, including GPU utilization, communication overhead, and memory
• Partner with research and modeling teams to align systems with algorithmic needs
• Evaluate and apply best practices for distributed training using industry-leading frameworks
• Dive deep into low-level optimization, including custom CUDA or Triton kernels
• Debug, profile, and fine-tune training workflows to unlock new levels of scalability

🎯 Requirements

• Strong background in LLMs, multimodal AI, or diffusion models
• Proficiency in Python. Familiarity with a system programming language (e.g. C++ or Rust) is a plus
• Deep knowledge of PyTorch or JAX as well as libraries such as Megatron-LM, NeMo, or DeepSpeed
• Familiarity with common optimization techniques such as FSDP/ZeRO, gradient checkpointing, or
• Hands-on experience writing custom GPU kernels in CUDA or Triton
• Excellent communication and problem-solving skills, incl. full proficiency in English