active · 2026
Databricks AI Intern
An autonomous AI and ML engineer that researches, trains, measures, reproduces, and serves what it builds using Databricks-native primitives.
- Databricks
- MLflow
- agents
- serverless
Give the system a goal such as “fine-tune this model on a governed table” or “build a classification model for this dataset.” It runs a measured research loop inside the workspace.
More than a one-shot agent
The control loop lives in code. Parallel hypotheses run as jobs, evaluation reads ground truth from governed data, and a reproduce gate prevents a lucky run from becoming the reported result.
Every experiment is written to a Delta ledger with MLflow lineage. The system can then plan serving infrastructure, deploy the selected model, and benchmark the endpoint.
The project explores a specific question: how much of an AI engineer’s iterative workflow can be automated without giving the model control over the evidence used to judge itself?