Quantitative Trading Technology Engineer – Multi-Strategy Hedge Fund – London – Up to £450k TC Full Time NEW

Quantitative Trading Technology Engineer – Multi-Strategy Hedge Fund – London – Up to £450k TC

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).
• Enhance and extend the risk‑modelling, pricing and analytics suite used directly on discretionary trading desks.
• Implement solid engineering practices: testing, code review, CI/CD, monitoring and documentation.
• Partner with front‑office users to deliver new tools, debug production issues and advocate best practices in how models and data are used in trading.

Candidate profile – research‑grade engineering

You have exceptional programming skills in Python (plus MATLAB) and are comfortable working on production systems, not just research scripts. You bring a deep quantitative toolkit – probability, statistics, optimisation and numerical methods – and you enjoy reading and implementing ideas from academic papers. You will typically have an outstanding academic record with a Master’s or PhD in a highly quantitative subject (Maths, Statistics, Computer Science, Physics, Engineering or similar) from a leading university; candidates without this background will need to demonstrate equivalent research‑level achievements in front‑office trading or risk technology. MATLAB experience is required, and familiarity with additional analytics tools (R, EViews) is a plus. Engineers on this team sit onsite with PMs and quants and largely come from research or PhD backgrounds, so you should be comfortable in a highly analytical, academic environment.

You should also have:

• Strong experience with scientific/data libraries and production systems.
• Solid SQL/RDBMS experience; time‑series DB experience (e.g. Timescale/PostgreSQL, kdb) is beneficial.
• A track record of owning projects end‑to‑end, from sitting with users through design and implementation to deployment and support.
• Strong communication skills and pragmatic problem‑solving in a fast‑moving front‑office setting.
• Prior front‑office or risk‑technology experience within a bank, asset manager or hedge fund is highly desirable.

Why now?

• You’ll be joining during a multi‑year build‑out of trading technology, not a maintenance phase. Small, focused teams mean meaningful ownership of systems that are core to how the fund trades. Compensation is highly competitive, with strong performance‑related upside and clear visibility of the impact of your work.