DataRobot

DataRobot is seeking a Deep Learning Research Engineer Intern to contribute to the development of a multi-target joint probabilistic foundation model. This ambitious project aims to create a single model capable of handling temporal forecasting, unordered tabular regression and classification, missing-data completion, and mixed-modality inputs and outputs, learning coherent joint structures across various dimensions. The primary focus is on generating real-world value, though strong benchmark performance is also expected. This internship offers a unique opportunity to shape the architecture of a probabilistic foundation model, working on encoders, attention mechanisms, sequence architectures, and distributional output heads. The role requires a blend of deep learning expertise, strong engineering habits, and the ability to reason precisely about model capabilities and limitations.

This internship focuses on enhancing the usefulness of AI agents in real-world applications by providing them with the right tools, context, and persistent memory across conversations and sessions. The core of the work involves the Model Context Protocol (MCP) and agent memory, exploring how agents discover and safely utilize tools, manage learned information, and remain helpful without constant supervision. The ultimate goal is to deliver practical value to daily agent users through clean, extensible implementations.