Hikaros Quant Lab (HQLab)
London, UK | Full-time | Research & Engineering
About Hikaros Quant Lab
Hikaros Quant Lab is quantitative investment fintech at the intersection of mathematics, machine learning, artificial intelligence and systematic investing.
Our ambition is to develop a new generation of quantitative research systems that combine exceptional human talent, advanced computational methods and AI-driven experimentation to discover, validate and deploy investment strategies.
We believe exceptional people are our greatest competitive advantage. We aim to create an environment where talented researchers and engineers have the freedom, technology, computing resources and intellectual challenge to solve difficult problems and turn ambitious ideas into measurable results.
The Role
As a Machine Learning Engineer at HQLAB, you will develop the models, algorithms and research infrastructure that power our quantitative investment strategies.
You will work at the intersection of machine learning research, quantitative finance and software engineering, developing methods to extract predictive signals from financial time series, identify market patterns and improve systematic trading strategies.
You will experiment with advanced ML techniques, design rigorous validation frameworks and build scalable systems that transform research hypotheses into deployable models.
Beyond individual models, you will help build our AI-native research engine, integrating specialised AI agents, automated experimentation and human expertise to accelerate the discovery, testing and refinement of investment strategies.
This is an opportunity to take meaningful technical ownership from day one, work on challenging open-ended problems and help shape the foundations of a new quantitative research company.
What You Will Do
- Develop machine learning models: Design, implement and evaluate models for financial time-series forecasting, market regime detection, signal generation and systematic trading.
- Explore advanced techniques: Apply deep learning, reinforcement learning, probabilistic modelling, Bayesian methods, representation learning and optimisation to quantitative problems.
- Build research pipelines: Develop reproducible workflows for data processing, feature engineering, model training, experimentation and performance evaluation.
- Engineer AI-driven research systems: Build components that enable AI agents to support hypothesis generation, experimentation, code development and research validation.
- Validate strategies rigorously: Apply robust statistical methods, out-of-sample testing, walk-forward validation and leakage controls to distinguish genuine predictive signals from noise.
- Optimise model performance: Improve computational efficiency, scalability, reliability and inference performance across research and deployment environments.
- Collaborate across disciplines: Work closely with quantitative researchers and software engineers to translate mathematical ideas into practical investment applications.
- Contribute to research culture: Review relevant scientific publications, challenge assumptions, share findings and continuously improve our research methodology.
Who We Are Looking For
We are looking for intellectually curious, mathematically strong engineers who enjoy solving difficult problems and building systems that work beyond the laboratory.
Essential qualifications and skills
- A strong academic background in computer science, machine learning, mathematics, statistics, physics, engineering or a related quantitative discipline.
- Strong Python programming skills and experience with libraries such as NumPy, Pandas, SciPy and scikit-learn.
- Practical experience developing, training, evaluating and improving machine learning models.
- A solid understanding of probability, statistics, linear algebra, optimisation and experimental design.
- Experience with software engineering fundamentals, including version control, testing, modular design and reproducible development.
- A rigorous approach to experimentation, critical thinking and evidence-based decision-making.
- The ability to communicate technical ideas clearly and collaborate effectively with multidisciplinary teams.
Desirable experience
- Experience with PyTorch, JAX, TensorFlow or other modern ML frameworks.
- Knowledge of financial markets, quantitative finance, algorithmic trading or financial time-series analysis.
- Experience with deep learning, reinforcement learning, transformers, probabilistic models or Bayesian inference.
- Familiarity with GPU computing, distributed training, cloud infrastructure or high-performance computing.
- Experience building LLM-powered applications, AI agents or automated research workflows.
- Publications, open-source contributions, research projects or strong results in technical competitions.
We value demonstrated ability over credentials alone. Exceptional candidates with outstanding projects, research contributions or a strong track record of independent problem-solving are encouraged to apply.
Why Join HQLAB?
Build from first principles. Help design the models, research infrastructure and technology that underpin a new quantitative investment company.
Work on difficult, meaningful problems. Investigate complex financial systems where rigorous experimentation and measurable performance matter.
Own your work. Take responsibility for ideas from initial hypothesis through implementation, validation and deployment.
Combine research with engineering. Work across machine learning, quantitative research and AI systems rather than being confined to a narrow technical function.
Help shape an AI-native research platform. Contribute to a system designed to accelerate scientific discovery through intelligent automation and human expertise.
Grow with the company. Help establish our technical foundations, research culture and engineering standards as HQLAB develops.
Collaborate with exceptional people. Join a team built around intellectual curiosity, high standards, continuous learning and shared ambition.
Our Principles
- Think Bigger: Pursue ambitious ideas and challenge conventional approaches.
- Execute with Excellence: Apply scientific rigour and engineering discipline.
- Act with Ownership: Take responsibility for outcomes, not just deliverables.
- Master Continuous Improvement: Learn, experiment and improve relentlessly.
Interested?
If you are passionate about machine learning, quantitative research and building technology that pushes boundaries, we would like to hear from you.
Tell us what you have built, what you have researched and which difficult problems you would like to solve.
Hikaros Quant Lab — Exceptional talent. Rigorous research. Intelligent investing.