About the company:
We’re working with a company building a large, proprietary hedge fund where technology is the core advantage. The team combines systematic trading, cutting-edge AI, and proprietary infrastructure to create a research environment where ideas can move rapidly from concept to live deployment. Working closely with an internal AI Research Lab, the team owns the entire lifecycle of quantitative research - from hypothesis generation and experimentation to production trading systems.
About the role:
We're looking for a researcher who will grow the RL direction within MFT: building and improving models that can find and monetize alpha in the market, not just explore it in theory.
Real commercial experience matters here: you have already built RL models for trading that ran live and generated profit, not ones that stayed as a prototype or a backtest on paper.
Going forward, you will help move from manually developed trading strategies to research infrastructure and agent-driven systems that generate, test, validate, and optimize strategies at scale on their own.
You will work at the intersection of market microstructure, quantitative research, machine learning, and system design, helping build research pipelines with continuous learning.
Real commercial experience matters here: you have already built RL models for trading that ran live and generated profit, not ones that stayed as a prototype or a backtest on paper.
Going forward, you will help move from manually developed trading strategies to research infrastructure and agent-driven systems that generate, test, validate, and optimize strategies at scale on their own.
You will work at the intersection of market microstructure, quantitative research, machine learning, and system design, helping build research pipelines with continuous learning.
Responsibilities:
- Develop and improve RL models for alpha generation in MFT, building on approaches that already work and are profitable
- Formalize the research process for trading: hypothesis spaces, validation logic, and the full lifecycle from idea to evaluation
- Design and build agent-driven research systems that autonomously generate, test, and optimize trading strategies
- Translate market microstructure intuition into machine-executable features, signals, and constraints that guide agent behavior
- Develop and adapt backtesting and simulation frameworks to support large-scale, autonomous experimentation under realistic execution conditions
- Increase research throughput: speed of hypothesis generation, exploration/exploitation balance, and statistical robustness of results
- Work with engineering to integrate agent-based research systems into production trading pipelines and continuously improve their performance
What we expect from you:
- Real commercial experience with RL in trading: at least one model you built and took live that generated profit
- 2+ years of experience in trading, with a clear understanding of how strategies are researched, validated, and deployed
- Deep understanding of market microstructure and exchange mechanics
- Experience with the full quantitative research lifecycle: hypothesis generation, backtesting, evaluation, iteration
- Solid grounding in probability, statistics, optimization, and practical research principles: data quality, leakage prevention, robustness, and reproducibility
- Proficiency in Python, ML frameworks, and data infrastructure, with the ability to write clean, efficient, and scalable code
- Ability to formalize, break down, and scale research processes into structured, repeatable workflows, metrics, and testing protocols suitable for automation
- Genuine interest in AI and agentic systems, with motivation to move from manual research toward building and scaling automated research agents
- English proficiency: B2 or higher
What's in it for you?
- Compensation is discussed individually, based on your expectations and experience
- Location: consider candidates based in Europe or Amsterdam, and are open to remote work
- Access to an exclusive computing architecture (an alternative to NVIDIA GPUs), with early access for the team
- Involvement in a research-driven project with real-world applications, rather than a standard production environment
- Work within a young, agile team with no bureaucracy, where decisions are made quickly
Send your CV on Telegram @veronikavlasovets