Temporal AI Research Intern: Forecasting & Foundation Models

Abel AI; Northern (KY); Hybrid; USD 18,000 - 30,000; Full time

Job summary

Abel AI is seeking interns to work on focused research or engineering projects at the core of its temporal intelligence stack. You will collaborate with researchers and engineers to scale foundation models, develop multimodal temporal models, and build agentic forecasting systems.

Ideal candidates are pursuing a Bachelors, Masters, or PhD in CS, ML, or related fields with strong ML/DL fundamentals and hands-on PyTorch and Python experience. Publications and large-scale ML experience are a plus.

Qualifications

• Pursuing a Bachelor’s, Master’s, or PhD in CS, Applied Math, Statistics, EE, ML or related fields.
• Strong ML and DL fundamentals required.
• Hands-on PyTorch experience and solid Python programming skills.
• Experience with time-series forecasting, LLMs, multimodal learning, and large-scale ML systems.

Responsibilities

• Own a focused research or engineering project and collaborate with our research and engineering teams.
• Contribute to scaling temporal foundation models and evaluate on large-scale datasets.
• Develop multimodal temporal models that combine text, time series, and structured signals.
• Build agentic forecasting systems that decompose questions and reason under uncertainty.
• Explore training and adaptation methods for temporal foundation models.
• Investigate causal discovery, causal inference and counterfactual reasoning for forecasting.
• Improve inference pipelines for low-latency forecasting across many tasks.
• Design experiments and benchmarks to understand model strengths and limitations.

Skills

ML fundamentals

Python programming

Time-series forecasting

Multimodal learning

Causal inference

LLMs / foundation models

Agentic AI systems

Large-scale ML systems

Education

Bachelor’s/Master’s/PhD in CS, Math, Stats, EE, ML

Tools

PyTorch

Python

Job description

Abel AI is seeking interns to work on focused research or engineering projects at the core of its temporal intelligence stack. You will collaborate with researchers and engineers to scale foundation models, develop multimodal temporal models, and build agentic forecasting systems.

Ideal candidates are pursuing a Bachelors, Masters, or PhD in CS, ML, or related fields with strong ML/DL fundamentals and hands-on PyTorch and Python experience. Publications and large-scale ML experience are a plus.