技能档案

Distributed Training

此技能定义了各角色和级别的期望。

Machine Learning & AI LLM & Generative AI

角色数

包含此技能的角色

级别数

结构化成长路径

必要要求

其余 5 个可选

Machine Learning & AI

LLM & Generative AI

2026/2/22

选择当前级别并对比期望。下方卡片显示晋升所需掌握的内容。

各级别期望

表格展示从初级到首席的技能深度变化。点击行查看详情。

角色	必要性	描述
LLM Engineer		Knows distributed training basics: DataParallel, model parallelism. Understands gradient synchronization concepts and runs simple multi-GPU training under mentor guidance on PyTorch.

角色	必要性	描述
LLM Engineer		Independently configures distributed training with DeepSpeed ZeRO and FSDP. Configures data parallel, pipeline parallel, and tensor parallel for models up to 7B parameters on GPU clusters.

角色	必要性	描述
LLM Engineer		Designs distributed training strategies for large LLM: 3D parallelism, ZeRO-3 offloading, activation checkpointing. Optimizes communication overhead and GPU utilization on 100+ GPUs.

角色	必要性	描述
LLM Engineer		Defines distributed training infrastructure for the LLM team. Establishes best practices for multi-node training configuration, monitoring and debugging distributed jobs on GPU clusters.

角色	必要性	描述
LLM Engineer		Shapes enterprise distributed training strategy. Defines approaches to scaling to 1000+ GPUs, cost optimization, and GPU resource planning for pre-training and fine-tuning.

Junior 1 要求

LLM Engineer

Knows distributed training basics: DataParallel, model parallelism. Understands gradient synchronization concepts and runs simple multi-GPU training under mentor guidance on PyTorch.

Middle 1 要求

LLM Engineer

Independently configures distributed training with DeepSpeed ZeRO and FSDP. Configures data parallel, pipeline parallel, and tensor parallel for models up to 7B parameters on GPU clusters.

Senior 1 要求

LLM Engineer

Designs distributed training strategies for large LLM: 3D parallelism, ZeRO-3 offloading, activation checkpointing. Optimizes communication overhead and GPU utilization on 100+ GPUs.

Lead / Staff 1 要求

LLM Engineer

Defines distributed training infrastructure for the LLM team. Establishes best practices for multi-node training configuration, monitoring and debugging distributed jobs on GPU clusters.

Principal 1 要求

LLM Engineer

Shapes enterprise distributed training strategy. Defines approaches to scaling to 1000+ GPUs, cost optimization, and GPU resource planning for pre-training and fine-tuning.

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