About the role#
This role is for a graduate research intern to contribute to the foundations of proactive intelligence. You will own a research problem end-to-end, from framing the initial question and developing methods to running experiments and publishing findings. The goal is to build systems that develop a deep understanding of customers and use that insight to make better decisions over time. You will work at the intersection of representation learning, foundation models, reinforcement learning, causal reasoning, and agentic systems, with the potential to ship your work into production systems used by millions.
What you'll do#
- Own a research problem end-to-end: framing the question, developing methods, running experiments, and publishing findings.
- Focus on areas such as customer world models, proactive intelligence, agentic decision systems, and learning from feedback loops.
- Develop systems that anticipate customer needs and initiate helpful actions before being asked.
- Build methods for continuous improvement from real-world outcomes.
- Evaluate and measure the performance and trust of the systems developed.
What you'll need#
- Currently enrolled in an MS or PhD program in Computer Science, Machine Learning, Statistics, Mathematics, Operations Research, or a related field, with the intent to return to the program after the co-op.
- Strong foundations in modern machine learning, including deep learning, optimization, representation learning, and foundation models.
- Fluency in Python and experience with PyTorch, JAX, or similar frameworks.
- Evidence of research excellence through publications, open-source contributions, technical leadership, or equivalent work.
- Experience conducting independent research and translating ideas into working systems.
Location & details#
- Term: Fall 2026 (8-month co-op).
- Work Modality: Remote.
- Location: Ontario, Canada.
- Status: Paid, full-time internship.
