Skill Profile

Distributed Training

This skill defines expectations across roles and levels.

Machine Learning & AI LLM & Generative AI

Roles

where this skill appears

Levels

structured growth path

Mandatory requirements

the other 5 optional

Machine Learning & AI

LLM & Generative AI

2/22/2026

Choose your current level and compare expectations. The items below show what to cover to advance to the next level.

What is Expected at Each Level

The table shows how skill depth grows from Junior to Principal. Click a row to see details.

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

Role	Required	Description
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.

Role	Required	Description
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.

Role	Required	Description
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.

Role	Required	Description
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 requirements

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 requirements

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 requirements

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 requirements

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 requirements

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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