Applied Research Intern, Proactive Intelligence & Customer World Models (PhD / Graduate Co-op)

Block•Toronto, ON

17h•Remote

About The Position

We're hiring a small cohort of graduate research interns to help build the foundations of proactive intelligence. This is not a traditional internship. You'll own a research problem end-to-end: framing the question, developing methods, running experiments, publishing findings, and, when successful, shipping your work into production systems used by millions of customers and sellers. You'll work at the intersection of representation learning, foundation models, reinforcement learning, causal reasoning, agentic systems, and product intelligence. The goal is not simply to build smarter models, but to build systems that develop a deeper understanding of customers and use that understanding to make better decisions over time. Past interns have shipped production systems within months and published their work in the same year.

Requirements

Currently enrolled in an MS or PhD program in Computer Science, Machine Learning, Statistics, Mathematics, Operations Research, or a related field, and returning to that program after the co-op.
Strong foundations in modern machine learning, including deep learning, optimization, representation learning, and foundation models.
Experience conducting independent research and translating ideas into working systems.
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.

Nice To Haves

Experience with large language models and agentic systems.
Experience with reinforcement learning, reward modeling, or sequential decision-making.
Experience with representation learning for structured, temporal, or graph data.
Familiarity with large-scale training and production ML systems.
Interest in building AI systems that directly affect customer outcomes.

Responsibilities

Framing research questions
Developing methods
Running experiments
Publishing findings
Shipping work into production systems
Building rich representations of customers from event streams, financial activity, operational signals, and behavioral data
Developing systems that can anticipate customer needs and initiate helpful actions before being asked
Building agents that reason over customer world models and take actions in real environments
Developing methods that allow intelligence to improve continuously from real-world outcomes
Building evaluation frameworks that predict real-world performance, trust, and customer value