What are the responsibilities and job description for the Graduate Quantitative Developer position at DeepFin Research?
DeepFin is a systematic proprietary trading firm combining deep learning, traditional quantitative research methods, and cutting-edge trading technology, to trade global markets. Founded by engineers and researchers, we build and deploy advanced trading systems that operate across global markets.
Our team is lean, highly technical, and impact-driven - every hire plays a direct role in shaping the firm’s technology, strategy, and performance. We value curiosity, precision, and collaboration, and we’re building an environment where exceptional people can do their best work at the intersection of AI and financial markets.
Junior Quant Developer — Backtesting, Simulation & Research, Productionise Research (C /Python)
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
Join us in building the next generation of deep-learning-driven trading systems - shaping the future of finance through innovation, rigour, and technology.
Our team is lean, highly technical, and impact-driven - every hire plays a direct role in shaping the firm’s technology, strategy, and performance. We value curiosity, precision, and collaboration, and we’re building an environment where exceptional people can do their best work at the intersection of AI and financial markets.
Junior Quant Developer — Backtesting, Simulation & Research, Productionise Research (C /Python)
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.
- 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.
Join us in building the next generation of deep-learning-driven trading systems - shaping the future of finance through innovation, rigour, and technology.