Grafton Sciences

Build and maintain scalable infrastructure for training, fine-tuning, RLHF/DPO workflows, and distributed experiments.
Develop data pipelines, dataset versioning systems, experiment tracking tools, and reproducibility frameworks.
Operate containerized inference and training environments, CI/CD for models, and evaluation automation.
Collaborate with LLM researchers, RL scientists, data engineering, and systems teams to support rapid iteration and robust model deployment.