ML Engineering · Machine Learning Engineer — Learning Repository

🎓 Part of the free, open-source AI Career Curriculum ecosystem — Infrastructure · ML Engineering · AI Engineering · Governance. Live cohorts & team programs: ai-infra-curriculum.github.io.

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Build and ship machine-learning models end-to-end: data curation, feature engineering, classical ML, deep learning with PyTorch, experiment tracking, evaluation, deployment, monitoring, and ML systems design. The build-altitude foundation of the ML Engineering ladder.

Status: curriculum plan landed (2026-06-25). 10 planned modules + 3 planned projects are defined in .aicg/curriculum-plan.json; see CURRICULUM.md for the readable map and JOB_REQUIREMENTS.md for requirement evidence. Lessons and projects will be drafted by subsequent autonomous content cycles. The first cycle should also gather ≥25 live job postings and backfill the requirement-frequency evidence — see the Status section of JOB_REQUIREMENTS.md.

Curriculum at a glance

• Role level: 20 (build-altitude, mid-career foundation).
• Modules: 10 (136 planned hours).
• Projects: 3 (125 planned hours).
• Total: ~261 hours.
• Ownership: owns the end-to-end ML practitioner workflow. Defers up to senior/staff/principal ML Engineer tracks for depth, sideways to peer specialist tracks (NLP, RAG, LLM application, fine-tuning, evaluation, training pipeline, MLOps, ML platform) for their domains, and down to the junior infrastructure track for engineering-craft prerequisites.

Module	Title	Hours
mod-101	ML Foundations	14
mod-102	Python ML Toolchain	12
mod-103	Data Engineering for ML	14
mod-104	Classical ML Modeling	16
mod-105	Deep Learning Fundamentals	18
mod-106	Experiment Tracking & Reproducibility	10
mod-107	Model Evaluation & Validation	12
mod-108	Model Packaging & Deployment	14
mod-109	ML Monitoring & Drift Detection	12
mod-110	ML Systems Design	14

Project	Title	Hours
project-101	End-to-End Tabular ML Project	40
project-102	Deep Learning Vision Project	35
project-103	ML System Capstone: Monitored Production Service	50

Layout

ml-engineer-learning/
├── lessons/mod-XXX-*/        modules with lectures, exercises, labs, quizzes
├── projects/project-XXX-*/   multi-module capstones
├── CURRICULUM.md             role-level coverage map
├── PREREQUISITES.md          assumed entry skills
├── JOB_REQUIREMENTS.md       requirement evidence + ownership table
├── VERSIONS.md               release history
├── .aicg/
│   ├── curriculum-plan.json  source-of-truth plan
│   └── job-requirements.json normalized requirements & references
└── README.md                 this file

Paired Solutions Repo

ml-engineer-solutions carries the reference implementations.

Related curricula

• Up the ladder: senior-ml-engineer-learning · staff-ml-engineer-learning · principal-ml-engineer-learning
• Peer specialist tracks: nlp-engineer-learning · rag-engineer-learning · llm-application-developer-learning · fine-tuning-engineer-learning · model-evaluation-engineer-learning · ai-eval-engineer-learning · training-pipeline-engineer-learning · applied-ai-engineer-learning
• Infra peers: ai-infra-engineer-learning · ai-infra-mlops-learning · ai-infra-ml-platform-learning · ai-infra-security-learning
• Prerequisite: ai-infra-junior-engineer-learning

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