Applied AI Research Engineer| Experienced Hire

Susquehanna International Group, LLP • Bala Cynwyd (Philadelphia Area), PA

17h

About The Position

Susquehanna is seeking an Applied AI Research Engineer to develop machine learning models that forecast market behavior from complex real-world data. The role focuses on noisy, non-stationary, low-signal-to-noise problems where standard approaches may require careful adaptation. The successful candidate will bring deep, demonstrable expertise in a specific problem area and be able to explain how their methods apply to the firm’s problems and where they may fail.

The engineer will work directly with traders to define relevant questions as well-scoped prediction problems and take resulting models from initial experiments through production. The role includes end-to-end ownership of environments, experimentation, deployment, and monitoring, as well as contributing to research practices in a small, flat team. Areas of work include LLMs, supervised fine-tuning, knowledge graphs, and other low-signal-to-noise forecasting tasks. The role suits a proactive researcher whose work can inform real decisions.

Requirements

• 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 shipped to production outside academia where real decisions depended on them, and ability to explain how their impact was measured.
• Rigorous experimental habits, including time-aware validation, leakage checks, strong baselines, and reproducible runs.
• Strong Python skills and hands-on experience with 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.

Responsibilities

• Research and develop machine learning models to improve trading strategies, focusing 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 their performance on live data.
• Partner directly with traders to turn open-ended questions into practical results.
• Own work end to end, including environments, experiments, deployment, and monitoring.
• Help shape research practices in a small, flat team.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior