PostTrainOps provides a unified workbench for post-training pipelines: experiment orchestration, evaluation metric tracking, distillation runs, and side-by-side comparisons of model quality and inference efficiency. It integrates with existing training and inference infrastructure so research and infra teams can test techniques from the literature, compare outcomes, and promote the best custom models into production.

Job postings show frontier AI companies hiring specifically for post-training, efficient inference, evaluation metrics, and custom model success. As more production traffic shifts to post-trained models, teams need repeatable tooling instead of bespoke research and infrastructure work for every model.