Loading
Turn data into intelligence.
AI/ML engineers build systems that learn from data — from training and fine-tuning models to deploying them in production. You work at the intersection of software engineering and machine learning, building RAG pipelines, embedding systems, and AI-powered features.
“You start by evaluating a new embedding model against your test suite. Mid-morning, you optimize a RAG pipeline's chunk size and retrieval strategy. After lunch, you debug why an AI agent is hallucinating on a specific edge case, write evaluation harnesses, and review a teammate's prompt engineering changes.”
10 required
16 required
24 required
When you complete this track, you'll have built:
NIST
Framework for managing risks in AI system design, development, and deployment.
European Commission
The first comprehensive legal framework for AI, classifying systems by risk level.
Google / Industry Practice
Documentation framework for trained ML models covering performance, bias, and intended use.
Roles you can grow into from here.