Job Description

Overview

We are looking for an Applied AI Research Engineer to build machine learning models that forecast market behavior from complex, real-world data. This role is focused on problems where signals are faint, labels are noisy, and the data shifts over time, so off-the-shelf approaches rarely work without careful adaptation to the structure of the problem. We want researchers with deep, clearly owned expertise in a specific problem area who can explain how their methods would carry over to ours, and where they would break.

You will work directly with traders to understand the questions that matter, turn them into well-scoped prediction problems, and carry the resulting models from first experiment to production. You will own your process end to end, from environments and experiments to deployment and monitoring, and help shape how research gets done in a small, flat team. The work spans LLMs, supervised fine-tuning, knowledge graphs, and other low signal-to-noise forecasting tasks.

This role is a strong fit for someone who is proactive, brings strong independent ideas, and wants to see their research drive real decisions.

What You’ll Do

• Research and develop ML models to improve trading strategies, with a focus on noisy, non-stationary, low signal-to-noise data
• Design and run rigorous, reproducible experiments to improve predictive signals and trading performance
• Take models from prototype to production, and monitor and diagnose how they perform on live data
• Partner directly with traders to turn open-ended questions into practical results

What we’re looking for

• PhD, MS, or Bachelor's degree in computer science, machine learning, statistics, mathematics, physics, or a related field
• Deep, hands-on expertise in an area such as language models, sequential modeling, or forecasting with noisy labels and non-stationary data
• Models you have shipped to production outside of academia, where real decisions depended on them, and the ability to speak to how you measured their impact
• Rigorous experimental habits, including time-aware validation, leakage checks, strong baselines, and reproducible runs
• Strong Python skills, with hands-on experience in Linux, Git with code review, and shared codebases
• Ability to reason about end-to-end system design, including bottlenecks and failure modes
• Ability to work independently in a fast-paced environment with evolving priorities
• Clear communication of technical work
• Curiosity about financial markets

About Susquehanna

Susquehanna is a global quantitative trading firm powered by scientific rigor, curiosity, and innovation. Our culture is intellectually driven and highly collaborative, bringing together researchers, engineers, and traders to design and deploy impactful strategies in our systematic trading environment. To meet the unique challenges of global markets, Susquehanna applies machine learning and advanced quantitative research to vast datasets in order to uncover actionable insights and build effective strategies. By uniting deep market expertise with cutting-edge technology, we excel in solving complex problems and pushing boundaries together.

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