About the role#
This is a graduate research internship focused on building 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. This role involves working at the intersection of representation learning, foundation models, reinforcement learning, causal reasoning, and agentic systems. The objective is to build systems that understand customer goals and context to make improved decisions over time.
What you'll do#
- Build rich representations of customer history, goals, and constraints using event streams, financial activity, and behavioral data.
- Develop systems that anticipate customer needs and initiate helpful actions before being asked.
- Create agentic systems capable of multi-step reasoning, tool use, and autonomous workflow execution.
- Design methods for continuous improvement through reinforcement learning, reward modeling, and preference learning.
- Build frameworks to evaluate system performance, trust, safety, and real-world customer value.
What you'll need#
- Current enrollment in an MS or PhD program in Computer Science, Machine Learning, Statistics, Mathematics, Operations Research, or a related field, with plans 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 practical experience with frameworks like PyTorch or JAX.
- Proven ability to conduct independent research and translate ideas into working systems.
- Evidence of research excellence through publications, open-source contributions, technical leadership, or equivalent work.
Location & details#
- Term: Fall 2026 (8-month duration).
- Work Modality: Remote.
- Employment Type: Full-time, paid internship.
- Location: Ontario, Canada.
How to get in at Block
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