Skill Profile

ML Experiment Tracking

This skill defines expectations across roles and levels.

Machine Learning & AI MLOps

Roles

where this skill appears

Levels

structured growth path

Mandatory requirements

the other 10 optional

Machine Learning & AI

MLOps

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
Data Scientist		Uses MLflow or W&B for basic experiment tracking: logging parameters, metrics, and artifacts. Structures experiments by projects, compares runs via UI. Saves models with metadata for reproducibility.
LLM Engineer		Knows experiment tracking basics: logging metrics, parameters, artifacts. Uses W&B or MLflow for tracking LLM training runs and fine-tuning experiments.

Role	Required	Description
Data Scientist		Independently builds experiment tracking workflow for ML projects. Integrates MLflow/W&B with training pipelines for automatic logging. Configures artifact storage, model registry, and experiment tags for work organization.
LLM Engineer		Independently organizes experiment tracking for LLM projects: structured projects in W&B, run comparison, hyperparameter sweeps. Versions datasets and model checkpoints.

Role	Required	Description
Data Scientist		Designs enterprise experiment tracking infrastructure. Integrates tracking with CI/CD, automated model promotion, and deployment. Configures multi-team collaboration, access control, and experiment governance for large-scale data science work.
LLM Engineer		Designs experiment tracking infrastructure for the LLM team: custom dashboards, automated reporting, CI/CD integration. Ensures reproducibility for all training and evaluation experiments.

Role	Required	Description
Data Scientist		Defines experiment management standards for the data science team. Establishes experiment review processes, knowledge sharing, and best practices. Coordinates experiment platform development and integration with ML infrastructure.
LLM Engineer		Defines experiment tracking standards for the LLM team. Establishes guidelines for experiment organization, naming conventions, and mandatory logging. Integrates tracking with model registry.

Role	Required	Description
Data Scientist		Shapes experiment management platform strategy for the organization. Defines enterprise requirements: compliance, audit trail, cost management. Evaluates tools (MLflow, W&B, Neptune, Vertex AI) and shapes long-term platform roadmap.
LLM Engineer		Shapes enterprise experiment tracking platform. Defines approaches to centralized tracking for multiple teams, cost management, and compliance with audit requirements for ML experiments.

Junior 2 requirements

Data Scientist

Uses MLflow or W&B for basic experiment tracking: logging parameters, metrics, and artifacts. Structures experiments by projects, compares runs via UI. Saves models with metadata for reproducibility.
LLM Engineer

Knows experiment tracking basics: logging metrics, parameters, artifacts. Uses W&B or MLflow for tracking LLM training runs and fine-tuning experiments.

Middle 2 requirements

Data Scientist

Independently builds experiment tracking workflow for ML projects. Integrates MLflow/W&B with training pipelines for automatic logging. Configures artifact storage, model registry, and experiment tags for work organization.
LLM Engineer

Independently organizes experiment tracking for LLM projects: structured projects in W&B, run comparison, hyperparameter sweeps. Versions datasets and model checkpoints.

Senior 2 requirements

Data Scientist

Designs enterprise experiment tracking infrastructure. Integrates tracking with CI/CD, automated model promotion, and deployment. Configures multi-team collaboration, access control, and experiment governance for large-scale data science work.
LLM Engineer

Designs experiment tracking infrastructure for the LLM team: custom dashboards, automated reporting, CI/CD integration. Ensures reproducibility for all training and evaluation experiments.

Lead / Staff 2 requirements

Data Scientist

Defines experiment management standards for the data science team. Establishes experiment review processes, knowledge sharing, and best practices. Coordinates experiment platform development and integration with ML infrastructure.
LLM Engineer

Defines experiment tracking standards for the LLM team. Establishes guidelines for experiment organization, naming conventions, and mandatory logging. Integrates tracking with model registry.

Principal 2 requirements

Data Scientist

Shapes experiment management platform strategy for the organization. Defines enterprise requirements: compliance, audit trail, cost management. Evaluates tools (MLflow, W&B, Neptune, Vertex AI) and shapes long-term platform roadmap.
LLM Engineer

Shapes enterprise experiment tracking platform. Defines approaches to centralized tracking for multiple teams, cost management, and compliance with audit requirements for ML experiments.

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