AI Infra Advisory Researcher

2 weeks ago

Beijing, Beijing, China Lenovo Full time
Job Title: AI Infra Advisory Researcher

Job Description:

Lenovo is seeking a highly skilled AI Infra Advisory Researcher to join our team. As a key member of our AI infrastructure team, you will be responsible for designing and developing high-performance AI infrastructure solutions that support large-scale model training.

Key Responsibilities:

1. Design and develop high-availability large model training systems that support trillion-parameter model pre-training.

2. Optimize large model training checkpoint recovery performance and improve checkpoint read/write and recovery efficiency.

3. Develop elastic large model training frameworks.

Requirements:

1. Master's degree or higher in Computer Science, AI, or related fields.

2. Proficient in C++/Python programming languages, data structures, and computer system structures, with experience in AI model performance tuning and good engineering implementation skills.

3. Familiar with common AI domain distributed training technologies, including but not limited to: data parallelism, pipeline parallelism, and tensor parallelism, with relevant project experience.

4. Familiar with at least one AI framework (PyTorch/TensorFlow/Paddle/DeepSpeed, etc.) and able to use and debug it proficiently.

5. Familiar with GPU hardware structure and CUDA computing principles, with CUDA-related operator development, debugging experience, and a basic understanding of NCCL/cuDNN.

6. Good understanding of large-scale pre-trained models, familiar with common pre-trained model structures, training methods, and optimization techniques.

7. Excellent problem-solving skills and innovative thinking, able to analyze and solve complex training problems, and propose improvement and optimization schemes.

8. Good teamwork spirit, able to work closely with cross-department teams to drive project success.

Preferred Qualifications:

1. Experience in large model development and distributed training.

2. Familiar with Kubernetes architecture and large model training checkpoint recovery systems.

3. Published high-level papers in AI or HPC fields.

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