ImpacT Hire is supporting one of our clients – a technology-driven High-Frequency Trading company operating in the cryptocurrency markets.
The company develops fully automated, low-latency trading systems and continuously improves its trading strategies, alpha generation processes, execution infrastructure, and Machine Learning research environment. Their technology operates directly in live markets, with new software and improvements deployed to production multiple times per day.
We are looking for a Quantitative Developer to work at the intersection of quantitative research, Machine Learning, software engineering, and High-Frequency Trading. The role focuses on researching predictive signals, developing and testing trading strategies, building realistic market simulations, and translating quantitative ideas into strategies that can perform in live trading environments.
This is a highly analytical and hands-on position where research is closely connected to production. You will work with large volumes of market data, experiment with statistical and ML approaches, evaluate ideas through rigorous backtesting, and continuously compare simulated performance with what actually happens in live markets.
Responsibilities
- Research and develop predictive signals and features aimed at forecasting short-term market behaviour and trade returns;
- Explore large volumes of historical and real-time market data to identify patterns, relationships, and potential sources of alpha;
- Design, train, and evaluate statistical and Machine Learning models for trading applications;
- Develop and improve quantitative trading strategies based on research findings;
- Build and run backtesting simulations using historical market data to evaluate strategy behaviour under realistic conditions;
- Tune strategy parameters and evaluate their robustness across different market conditions;
- Model trade execution and fills in order to reduce the gap between simulated and real-world strategy performance;
- Analyse transaction costs, execution behaviour, latency effects, and other factors that can influence live trading results;
- Reconcile production orders and trading results against backtesting and simulation outputs, investigating discrepancies between expected and actual behaviour;
- Continuously monitor and evaluate live strategy performance and identify opportunities for improvement;
- Generate new trading hypotheses and rapidly test whether they have measurable predictive or economic value;
- Translate successful research ideas into robust implementations suitable for automated, latency-sensitive production environments;
- Collaborate closely with quantitative researchers and software engineers working on trading systems, research infrastructure, and execution technology;
- Contribute to improvements in research tooling, simulation infrastructure, and the broader alpha-generation process.
Requirements
- Advanced degree in Computer Science, Mathematics, Physics, Engineering, or another highly quantitative discipline;
- Strong mathematical, statistical, and analytical background;
- Knowledge of or practical experience with High-Frequency Trading, algorithmic trading, quantitative trading, or electronic markets;
- Minimum of 1 year of profecional experince in C++ or another high-performance programming language;
- Strong understanding of algorithms, data structures, computational complexity, and numerical methods;
- Deep understanding of different Machine Learning algorithms and their practical application to data-driven problems;
- Experience working with large datasets and performing rigorous quantitative analysis;
- Strong understanding of backtesting, model validation, and the importance of avoiding overfitting and misleading research results;
- Ability to approach problems through probabilistic and data-driven thinking, quickly test hypotheses, and distinguish promising ideas from those that do not create measurable value;
- Strong problem-solving skills and ability to learn and apply concepts from different technical and quantitative domains;
- Fluency in English.
Nice to have
- Previous professional experience in HFT, quantitative trading, market making, systematic trading, or a similar environment;
- Experience working with cryptocurrency markets and digital-asset market structure;
- Strong C++ knowledge and experience developing performance-sensitive or low-latency software;
- Understanding of CPU architecture, concurrency, memory models, and multithreading – areas that are also central to the company’s low-latency engineering environment;
- Understanding of order books, market microstructure, execution strategies, slippage, transaction costs, and exchange behaviour;
- Experience developing or improving research and backtesting infrastructure;
- Experience with AWS or other cloud environments;
- Exposure to real-time, high-performance, or distributed systems.
What they offer
- Opportunity to work directly on High-Frequency Trading strategies and live automated trading systems;
- Close connection between quantitative research and production – successful ideas can directly influence live trading;
- Technically challenging environment combining quantitative research, Machine Learning, low-latency engineering, and financial markets;
- Opportunity to work with complex market data and continuously experiment with new models, signals, and trading approaches;
- High level of individual ownership and direct visibility into the impact of your work;
- Research-driven culture focused on experimentation, measurable results, and continuous improvement;
- Competitive compensation package;
- Annual performance-based bonus.
The team values people who combine creative problem-solving with probabilistic thinking – engineers and researchers who enjoy learning new concepts, testing ideas with data, and focusing their effort on solutions that create measurable value rather than treating research as a purely academic exercise.
If you enjoy combining mathematics, Machine Learning, high-performance software engineering, and financial markets, this role offers the opportunity to work on quantitative strategies where research, technology, and live market performance are directly connected.