We’re partnering with a technology-first hedge fund where technology is the core of the business. The company’s key assets include: • Trading infrastructure for both crypto and traditional markets • AI models and research pipelinesused directly in trading decision-making • Next-generation computing systems, including experiments with novel hardware and performance-critical software The fund builds trading systems at the intersection of financial markets, AI, and frontier technologies. It operates its own AI research lab, has stable long-term funding, and access to experimental hardware. Unlike traditional hedge funds, researchers have direct influence on both research methodology and production systems.
What you’ll do:
Strategy Research & Portfolio Development:
Lead research and development of systematic mid-frequency strategies across equities, futures, ETFs, options, and FX, taking alpha ideas from generation and validation through to design of predictive signals, portfolio construction models, and execution logic;
Improve existing strategies through live performance analysis, optimizing capacity, turnover, transaction costs, and risk-adjusted returns, and evaluate new and alternative datasets.
Portfolio Construction & Risk:
Build portfolio optimization and capital allocation frameworks, robust risk models, and exposure controls, analyzing factor exposures, drawdowns, turnover, and capacity;
Improve execution quality through transaction cost analysis, slippage and market impact modeling, and build monitoring metrics for production strategies.
Research Leadership:
Lead and mentor researchers, participate in hiring, and build a high-performance culture grounded in scientific rigor;
Establish research standards, validation procedures, and experiment methodology, reviewing proposals and challenging assumptions.
Production & Collaboration:
Partner with engineers to deploy research into production, defining requirements for infrastructure, data pipelines, and simulation frameworks, and improve backtesting so trading costs and exchange behavior are realistic;
Monitor live strategies, drive continuous improvements, and contribute to the long-term research and technology roadmap.
You would be a great fit if you have:
Experience in quantitative research for systematic trading;
Proven track record of developing profitable mid-frequency trading strategies from idea to production;
Experience with traditional financial markets such as equities, futures, options or FX;
Great understanding of market microstructure, statistical modeling, time-series analysis, portfolio optimization, execution algorithms, transaction cost modeling;
Excellent Python programming skills;
Experience working with large market datasets;
Mathematical background in probability, statistics and optimization;
Experience mentoring researchers or leading research projects.
Candidates may also have experience with:
C++ engineering;
Machine learning applied to alpha generation;
Alternative data research;
Reinforcement learning;
GPU computing;
Experience building research platforms or backtesting infrastructure;
Experience managing production systematic portfolios.
Location & Format:
Hybrid work format in Amsterdam;
Full relocation support is provided.
What makes this opportunity different:
Work at the intersection of trading, AI, and frontier technologies;