- Location
- New York, NY, US
- Workplace
- Onsite
- Department
- IT
- Education
- PhD
- Closing date
- Today
- Source
- iCIMS
Description
Overview
Join us for a two-day Discovery Summit designed for students studying machine learning who are curious about what happens when research meets the markets.
Susquehanna is a global quantitative trading firm powered by scientific rigor, curiosity, and innovation. Our machine learning teams develop new approaches for prediction and decision-making, from deep learning and reinforcement learning to agentic AI and build the high-performance systems that bring those ideas to life at scale. At the summit you will hear directly from our researchers and engineers about how they apply machine learning techniques, such as deep learning, sequence modeling, and large-scale optimization to noisy, fast-moving financial data, where the signal is faint and the feedback loop is immediate. You will hear directly from our machine learning researchers and engineers, and gain exposure to the real problems they work on and explore how models move from experiment to live trading.
Throughout the summit, you will:
- Work through technical sessions on options theory, financial data, and machine learning in practice
- Meet senior researchers and engineers and hear how they approach open-ended problems
- Take part in trading games that put probabilistic thinking and decision making under uncertainty to the test
- Connect with junior researchers who made the move from academia to industry
- Tour our trading floor
Whether you are exploring new applications for your research or weighing a transition out of academia, this is a close look at the intellectual challenges and collaborative culture of our machine learning teams.
What we’re looking for
This opportunity is for individuals currently pursuing a Bachelor's, Master's, or PhD in computer science, machine learning, electrical engineering, mathematics, physics, statistics, or a related technical field. We are especially interested in candidates with:
- Hands-on experience building, training, and evaluating machine learning models
- A track record of in-depth research, ideally with publications or substantial independent projects
- Strong programming skills and comfort working with large datasets
- Curiosity about applying rigorous research methods to messy, real-world data
You do not need a finance background. We will teach you what you need to know about the markets.