ml-intern is an open-source autonomous agent that takes over the repetitive engineering work of ML post-training. It reads arXiv papers to surface relevant techniques, generates and repairs datasets, submits and monitors training jobs, diagnoses failures, and re-runs experiments on its own — closing a loop that normally requires a dedicated team. In independent benchmarks, it delivered a +22 point GPQA improvement in under 10 hours and a +60% gain on HealthBench. Designed for ML researchers and engineers who want to compress iteration cycles, ml-intern runs headlessly, logs its reasoning at each step, and can be pointed at any post-training objective. Being fully open-source, it's inspectable and forkable for custom training pipelines.