Graduate Quantitative Developer

Develop high-performance C++ trading systems by turning quantitative research into production-ready solutions for algorithmic trading.

Role Overview

We’re hiring a junior Quant Developer to help productionise research into robust, high-performance trading systems. You’ll work closely with Quant Researchers and senior engineers to convert Python research code into production C++, build and optimise backtesting / simulation infrastructure, and support strategy development using L3 market data across multiple venues.

This is a hands-on, engineering-heavy role in a fast-moving environment: you’ll own components end-to-end and contribute directly to research velocity and trading PnL.

Key Responsibilities

• Productionise research models into C++: translate Python prototypes into efficient, maintainable C++ production code.

• Backtesting & simulation: build and improve simulation systems that reflect real market mechanics (order book, fills, cancels, exchange rules).

• L3 market data handling: ingest and process high-volume tick/order-level feeds; create reliable feature pipelines from raw exchange data.

• Performance optimisation: improve latency and throughput of backtests/sims (profiling, memory optimisation, data structures, parallelism where appropriate).

• Research support tooling: create utilities for data inspection, experiment tracking, run orchestration, and post-trade analytics in Python.

• Debugging & correctness: investigate mismatches between simulation and production behaviour; diagnose edge cases and implement fixes with strong test coverage.

• Cross-team collaboration: work daily with researchers and infra/exec engineers to ship improvements from idea → test → production.

Requirements

• Education: Bachelor’s or Master’s from a top university in Computer Science, Engineering, Math, Physics, or similar.

• 0-3 years experience in quantitative finance or other relevant data-intensive industries working with C++

• Strong working knowledge of C++ (memory, ownership, STL, performance-aware coding).

• Experience: demonstrable evidence of hands-on systems work in C++ handling large-scale data (internships, research labs, competitive projects, open-source).

• Comfortable with Python for analysis, tooling, and debugging (pandas/numpy/Jupyter a plus).

• Exposure to quantitative finance, eg through internships/university societies, including market microstructure and L3/order book data.

• Clear “builder mindset”: you like owning problems end-to-end, shipping incrementally, and iterating quickly.

If you’re passionate about applying advanced technology to real-world markets and want to work alongside a focused, high-performing team, we’d love to hear from you. DeepFin offers a collaborative, research-driven environment where ideas move quickly from concept to execution and where every contribution has visible impact.