Description
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.
Depending on your interests and Apollo's roadmap, you'll focus on one or more of the following areas:
- Customer World Models: Building rich representations of customers from event streams, financial activity, operational signals, and behavioral data.
- Proactive Intelligence: Developing systems that can anticipate customer needs and initiate helpful actions before being asked.
- Agentic Decision Systems: Building agents that reason over customer world models and take actions in real environments.
- Learning from Feedback Loops: Developing methods that allow intelligence to improve continuously from real-world outcomes.
- Evaluation and Measurement: Building evaluation frameworks that predict real-world performance, trust, and customer value.
Required skills:
- 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 have:
- 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.
What you'll get:
- Direct mentorship from researchers working on the future of proactive intelligence at Block.
- Access to large-scale datasets, modern infrastructure, frontier models, and substantial compute resources.
- Opportunities to publish and contribute to open-source projects.
- A chance to shape foundational technology that could power the next generation of Block products.
- Exposure to both scientific research and product deployment, with a clear path from idea to impact.