A technology-first hedge fund is looking for a CTO. The company already operates:
Trading infrastructure for both crypto and traditional markets
AI models and research pipelines directly applied to trading decision-making
Next-generation computing systems, including experiments with novel hardware and performance-critical software
The firm 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 provides access to experimental hardware.
Key Objectives:
Launch an HFT division from scratch, with no legacy constraints or inherited architectural decisions
Bridge AI/ML research and engineering into fully operational trading systems
Design scalable architecture with direct impact on P&L
Provide technological leadership and grow the engineering team
Here, research and production are not separate - they form a unified engineering system where architectural decisions have measurable real-world impact.
Areas of Responsibility:
Design and launch low-latency trading infrastructure for crypto and traditional markets
Build infrastructure for training, scaling, and production deployment of ML/AI models
Develop the software layer for novel hardware (compilers, runtimes, performance-critical systems)
Integrate research pipelines into production trading systems
Conduct architectural audits of existing systems and define the target technical landscape
Participate in building and developing the engineering team
Candidate Profile:
Experience building trading / HFT infrastructure or systems with comparable latency and reliability requirements
Strong systems engineering background and deep understanding of high-load system architecture
Practical knowledge of ML models, training/inference workflows, and MLOps
Proven experience designing complex systems from scratch
Strong technical and managerial leadership skills, with the ability to work alongside highly skilled engineers and researchers
Ability to make architectural decisions in environments with high uncertainty
Location & Format:
Primary location: Amsterdam
Remote within Europe possible, with regular travel to the Netherlands