Perfil de habilidad

Distributed Training

Esta habilidad define expectativas en roles y niveles.

Machine Learning & AI LLM & Generative AI

Roles

donde aparece esta habilidad

Niveles

ruta de crecimiento estructurada

Requisitos obligatorios

los otros 5 opcionales

Machine Learning & AI

LLM & Generative AI

22/2/2026

Selecciona tu nivel actual y compara las expectativas.

Qué se espera en cada nivel

La tabla muestra cómo crece la profundidad desde Junior hasta Principal.

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

Rol	Obligatorio	Descripción
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.

Rol	Obligatorio	Descripción
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.

Rol	Obligatorio	Descripción
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.

Rol	Obligatorio	Descripción
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 requisitos

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 requisitos

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 requisitos

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 requisitos

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 requisitos

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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