Research Scientist / AI Engineer Intern — Temporal Intelligence

Role overview Take ownership of a focused research or engineering project advancing AI systems that model change over time and forecast future outcomes. Working with a research and engineering team, you may contribute across model training, multimodal and agentic forecasting, causal reasoning, inference, or evaluation.

Responsibilities - Help train and evaluate temporal foundation models, including experiments on how data, model, and compute scale affect forecasting. - Develop approaches that combine text, time series, tabular data, events, and other structured signals. - Build forecasting agents that retrieve relevant information, use models and tools, assess uncertainty, and synthesize answers. - Investigate pre-training, post-training, fine-tuning, and adaptation techniques for temporal models. - Explore causal discovery, inference, and counterfactual reasoning for better forecasts and decisions. - Design experiments and improve efficient inference or model-serving pipelines.

Requirements - Ability to take on a defined research or engineering project and work closely with a technical team. - Strong curiosity about temporal dynamics, forecasting, causality, or decision-making. - Ability to build, run careful experiments, interpret results, and learn quickly. - Relevant experience or demonstrated interest in machine learning, AI engineering, or research; the project scope can vary with background.

Nice to have - Experience training or fine-tuning language models, time-series models, or other large-scale models. - Work with large time-series or tabular datasets, distributed training, GPU clusters, or inference optimization. - Experience building agents with tool use, retrieval, planning, code execution, or model orchestration. - Research or open-source contributions in forecasting, temporal or causal learning, multimodal AI, or generative modeling. Publications are a plus, not a requirement.

Benefits and work setup This is a remote internship, with the listed location covering the US and global candidates. The project is intended to connect to core research or engineering work and may contribute to research, open-source systems, or production models.