We're looking for a Middle+/Senior HFT Researcher to join an ambitious AI startup tackling a problem no one has solved yet.
This is a closed research laboratory at the intersection of foundational AI and quantitative finance. Its mission is to build a unified foundation model capable of simultaneously understanding language and time series – treating text and numerical data as parts of a single coherent reality.
Originally developed as an integrated AI research lab with an internal hedge fund, the organization is now launching a high-frequency trading initiative built entirely from scratch – without legacy systems or off-the-shelf components. The goal is to close the loop from research hypothesis to microsecond-scale execution through an end-to-end AI-driven pipeline.
Responsibilities:
Research and prototyping of short-horizon trading strategies on liquid markets
Working with high-frequency market data: feature engineering, market microstructure analysis
Development and evaluation of models (statistics, time series, ML where measurable value is present)
Backtesting accounting for real-world costs and constraints
Data preparation: from collection to transformation
Collaboration with engineers to deploy strategies to production (the researcher does not perform deployment directly)
Responsibilities
Design, build, and maintain production-grade LLM systems;
Own the full LLMOps lifecycle: data preparation, fine-tuning, evaluation, deployment, monitoring, and iteration;
Build evaluation frameworks to track quality, robustness, and regressions;
Work closely with researchers and engineers to productionize research prototypes;
Contribute to architecture decisions for agentic systems, RAG pipelines, and hybrid ML + symbolic solutions.
Required Skills & Experience:
Ideal: commercial experience in HFT, especially at leading trading firms
Also considered: relevant personal projects, academic research focused on market microstructure or quantitative finance
Strong machine learning skills with practical application to high-frequency data
Understanding of market microstructure and exchange mechanics
Solid foundation in statistics, probability theory, and optimization
Confident Python programming skills, experience with ML frameworks, attention to code efficiency
Location: Montreal (Canada), the Netherlands, Serbia, Cyprus, and Dubai. Official employment. OR remote work under a B2B contract from the European time zone
Relocation support – available for the Netherlands
Access to an exclusive computing architecture (an alternative to NVIDIA GPUs), for which the team will have early access
Involvement in a research-driven project with industrial applications, rather than a standard production environment.
Work within a young, agile team with no bureaucracy, where decisions are made quickly and efficiently.