About the role#
This position is for graduate students pursuing an MS or PhD who are interested in 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. Successful projects may be integrated into production systems used by millions of customers and sellers. You will work at the intersection of representation learning, foundation models, reinforcement learning, causal reasoning, and agentic systems.
What you'll do#
- Build rich representations of customers from event streams, financial activity, operational signals, and behavioral data.
- Develop systems that anticipate customer needs and initiate helpful actions before being asked.
- Create agents that reason over customer world models and take actions in real environments.
- Develop methods that allow intelligence to improve continuously from real-world outcomes.
- Build evaluation frameworks to predict real-world performance, trust, and 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.
- Proven 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.
Location & details#
- Term: Fall 2026.
- Work Modality: Remote.
- Employment Type: Full-time.
- Compensation: Paid internship.
- Sponsorship: Not available.
How to get in at Block
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