Skill-Profil

Distributed Training

Dieser Skill definiert Erwartungen über Rollen und Level.

Machine Learning & AI LLM & Generative AI

Rollen

wo dieser Skill vorkommt

Stufen

strukturierter Entwicklungspfad

Pflichtanforderungen

die anderen 5 optional

Machine Learning & AI

LLM & Generative AI

22.2.2026

Wählen Sie Ihr aktuelles Level und vergleichen Sie die Erwartungen.

Was wird auf jedem Level erwartet

Die Tabelle zeigt, wie die Tiefe von Junior bis Principal wächst.

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

Rolle	Pflicht	Beschreibung
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.

Rolle	Pflicht	Beschreibung
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.

Rolle	Pflicht	Beschreibung
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.

Rolle	Pflicht	Beschreibung
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 Anforderungen

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 Anforderungen

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 Anforderungen

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 Anforderungen

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 Anforderungen

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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