A hands-on, role-based curriculum for the ML Engineering career ladder.
The content in these repositories is generated with AI assistance and undergoes ongoing human review. It may contain errors or outdated information. Treat it as a learning resource: cross-reference official docs, test code in a safe environment, and report issues via GitHub Issues or Discussions.
| Role | Level | Repositories |
|---|---|---|
| Machine Learning Engineer | L20 | 📘 Learning · ✅ Solutions |
| Fine-Tuning Engineer | L30 | 📘 Learning · ✅ Solutions |
| Model Evaluation Engineer | L30 | 📘 Learning · ✅ Solutions |
| NLP Engineer | L30 | 📘 Learning · ✅ Solutions |
| Senior Machine Learning Engineer | L30 | 📘 Learning · ✅ Solutions |
| Training Pipeline Engineer | L35 | 📘 Learning · ✅ Solutions |
| Staff Machine Learning Engineer | L40 | 📘 Learning · ✅ Solutions |
| Principal Machine Learning Engineer | L48 | 📘 Learning · ✅ Solutions |
See the Career Progression Guide for the full ladder — level descriptions, skills matrix, compensation ranges, promotion criteria, and specialist tracks.
One of four sibling orgs in the AI Career Curriculum ecosystem, organized by what you do relative to a model:
- AI Infrastructure Curriculum — run the platforms (Kubernetes, GPUs, training infra, serving, MLOps, IaC, SRE).
- ML Engineering Curriculum (you are here) — build & train the models (data, fine-tuning, pretraining, RLHF, evals).
- AI Engineering Curriculum — build with the models (agentic apps, RAG, multi-agent systems).
- AI Governance Curriculum — govern & assure AI (security, compliance, evaluation, safety).
Maintained by VeriSwarm.ai