Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Design and build cutting-edge trading technology for a high-performing hedge fund.
- Company: Dynamic multi-strategy hedge fund with a focus on innovation and growth.
- Benefits: Competitive salary up to £450k, collaborative environment, and career advancement opportunities.
- Other info: Exciting projects with a focus on AI-driven tools and real-time data integration.
- Why this job: Join a small team where your work directly impacts trading success and profitability.
- Qualifications: Strong Python skills and experience in low-latency systems; C++ knowledge is a plus.
A high‑performing, multi‑strategy hedge fund of around 380 people globally is in a genuine build phase across its Trading Technology platform. The firm is scaling beyond 400 people but deliberately avoiding “mega‑fund” scale, so small engineering teams still own critical systems end‑to‑end and see a direct link between what they build and P&L. Around 30 hires into Technology are planned for 2026, with a particular focus on discretionary trading tech, data, and early AI‑driven tooling.
The Role – High‑Ownership Trading Tech
You will be a senior, hands‑on engineer embedded with discretionary trading teams, owning key components of the trading stack rather than working in a distant central IT group. You will:
- Turn research and desk models into robust, low‑latency, production services used live by PMs and traders.
- Design and own real‑time and end‑of‑day risk, P&L and pricing tools that directly support trade sizing and execution.
- Build and evolve scalable market and reference data pipelines, integrating vendor feeds and internal systems.
- Work directly with Portfolio Managers, quants and Risk to shape requirements, prioritise projects and iterate quickly on desk tools.
Example projects:
- Re‑engineering a trader’s prototype Python script into a monitored microservice behind the order‑management / blotter stack.
- Building an intraday scenario engine so PMs can stress portfolios across rates, FX, credit and equity derivatives in seconds.
- Developing a real‑time exposure dashboard with alerting that hooks into existing trading workflows.
- Collaborating with data and AI engineers on intelligent monitoring or research‑support tools (e.g. automated checks on model behaviour, smarter analytics surfaces).
Key Responsibilities:
- Design, build and maintain high‑quality Python services and libraries (Pandas, NumPy; C++ exposure a plus for performance‑critical paths).
Quantitative Trading Technology Engineer – Multi-Strategy Hedge Fund – London – TC employer: Mondrian Alpha
Contact Detail:
Mondrian Alpha Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Quantitative Trading Technology Engineer – Multi-Strategy Hedge Fund – London – TC
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with professionals on LinkedIn. The more connections we make, the better our chances of landing that dream role.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to trading tech or data pipelines. This gives us a chance to demonstrate our hands-on experience and problem-solving abilities.
✨Tip Number 3
Prepare for technical interviews by brushing up on Python and any relevant tools. We should practice coding challenges and system design questions to show we can build robust, low-latency services.
✨Tip Number 4
Apply through our website! It’s the best way to ensure our application gets noticed. Plus, it shows we’re genuinely interested in being part of the team and contributing to the exciting build phase.
We think you need these skills to ace Quantitative Trading Technology Engineer – Multi-Strategy Hedge Fund – London – TC
Some tips for your application 🫡
Show Your Passion for Trading Tech: When writing your application, let us see your enthusiasm for trading technology. Share any relevant projects or experiences that highlight your skills in building low-latency services or working with trading teams. We love seeing candidates who are genuinely excited about the role!
Tailor Your CV and Cover Letter: Make sure to customise your CV and cover letter for this specific role. Highlight your experience with Python, data pipelines, and any hands-on work with trading systems. We want to see how your background aligns with our needs, so don’t hold back on the details!
Be Clear and Concise: Keep your application clear and to the point. Use bullet points where possible to make it easy for us to read through your achievements and skills. We appreciate a well-structured application that gets straight to the good stuff!
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re serious about joining our team at StudySmarter!
How to prepare for a job interview at Mondrian Alpha
✨Know Your Tech Inside Out
Make sure you’re well-versed in Python and any relevant libraries like Pandas and NumPy. Brush up on your understanding of low-latency systems and how they impact trading. Being able to discuss specific projects you've worked on, especially those involving real-time data or microservices, will show your hands-on experience.
✨Understand the Trading Environment
Familiarise yourself with the multi-strategy hedge fund landscape and the role of discretionary trading tech. Be prepared to discuss how your work can directly influence P&L and risk management. Showing that you understand the business side of trading will set you apart from other candidates.
✨Prepare for Technical Questions
Expect to face technical questions that test your problem-solving skills and coding abilities. Practice coding challenges related to data pipelines and real-time systems. You might be asked to re-engineer a piece of code or design a system on the spot, so be ready to think critically and articulate your thought process.
✨Showcase Collaboration Skills
Since you'll be working closely with Portfolio Managers and quants, highlight your experience in cross-functional teams. Prepare examples of how you've gathered requirements and iterated on projects based on feedback. Demonstrating your ability to communicate effectively with non-technical stakeholders will be key.