About the role#
This internship focuses on building the foundations of proactive intelligence within the Apollo team. You will own a research problem end-to-end, from framing the initial question and running experiments to publishing findings and shipping work into production systems. You will work at the intersection of representation learning, foundation models, reinforcement learning, causal reasoning, and agentic systems to develop technology that anticipates customer needs and improves decision-making over time.
What you'll do#
- Own a research problem end-to-end: framing the question, developing methods, running experiments, and publishing findings.
- Focus on building rich representations of customers from event streams, financial activity, operational signals, and behavioral data.
- Develop systems that can anticipate customer needs and initiate helpful actions before being asked.
- Build 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.
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 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.
- 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 (8-month co-op).
- Modality: Remote.
- Employment: Full-time, paid internship.
