Reinforcement Learning Researcher (Trading)

Location: Europe or Amsterdam; remote work is available

Company

A technology-driven proprietary hedge fund combining systematic trading, AI, and proprietary infrastructure to develop and deploy quantitative strategies.

What you will do

• Develop and improve reinforcement learning models for profitable alpha generation in medium-frequency trading.
• Formalize the trading research lifecycle, including hypothesis generation, validation, evaluation, and iteration.
• Design agent-driven systems that autonomously generate, test, and optimize trading strategies.
• Translate market microstructure knowledge into executable features, signals, and behavioral constraints.
• Build and adapt backtesting and simulation frameworks for large-scale experimentation under realistic execution conditions.
• Integrate agent-based research systems into production trading pipelines and improve research throughput and statistical robustness.

Requirements

• Commercial experience building reinforcement learning models for trading, including at least one live model that generated profit.
• At least 2 years of trading experience and understanding of strategy research, validation, and deployment.
• Deep knowledge of market microstructure and exchange mechanics.
• Experience with quantitative research, backtesting, probability, statistics, optimization, data quality, leakage prevention, robustness, and reproducibility.
• Proficiency in Python, machine learning frameworks, and data infrastructure, with the ability to write clean, efficient, and scalable code.
• English proficiency: B2 or higher; candidates should be based in Europe or Amsterdam, with remote work available.

Culture & Benefits

• Access to an exclusive computing architecture that provides an alternative to NVIDIA GPUs, with early access for the team.
• Participation in a research-driven project with real-world applications rather than a standard production environment.
• Young, agile team with low bureaucracy and fast decision-making.
• Compensation is discussed individually based on experience and expectations.