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.

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.

HFT Software Developer – Junior & Senior Levels

About DeepFin Research

DeepFin Research is a proprietary high-frequency trading (HFT) firm powered by cutting-edge Deep Learning (DL) and Deep Reinforcement Learning (DRL). We've brought on teammates from Nvidia, DeepMind, CitSec, Graviton, Tower, Jump, and others, and are aggressively working across cutting edge AI research and traditional quant research methods to monetise our AI generated signals across the global financial markets.

The Role

We are looking for exceptional HFT software engineers to build our HFT trading infrastructure across global finanacial markets, including different asset classes, end-to-end from the ground up. This will include working across connectivity, market data, execution, risk, and building high performance high throughput C++ systems.

Responsibilities

• Design, implement, and optimize ultra-low-latency, zero-allocation trading engines in modern C++.
• Develop and maintain market data handlers and execution gateways utilizing protocols like FIX, OUCH, ITCH, and raw UDP multicast.
• Optimize system critical paths through OS-level tuning, CPU pinning, cache-line optimization, and NUMA-aware programming.
• Interface with hardware-accelerated components, working alongside FPGA engineers to integrate PCIe/DMA data pipelines.
• Implement kernel-bypass networking solutions (e.g., Solarflare OpenOnload, DPDK) to minimize network stack latency.
• Collaborate directly with AI researchers and quantitative traders to deploy complex quantitative models into strict, latency-sensitive production environments.
• Work end-to-end on projects from the ground up – this is not a role to focus on specialised parts of the infrastructure stack, but rather to see projects through end-to-end.

Ideal Candidate Profile

• 1–3 years of experience in low-latency software engineering for Junior candidates; 3–10+ years for Senior candidates.
• Exceptional expertise in C++ with a deep understanding of lock-free data structures, custom memory allocators, and hardware architecture.
• Proven experience building systems in HFT or highly concurrent, real-time computing environments.
• Strong grasp of Linux internals, network programming (TCP/IP, UDP), and performance profiling tools (e.g., perf, VTune).
• Solid understanding of futures markets microstructure; exposure to FX and options markets is a strong plus.
• Entrepreneurial mindset with the communication skills required to thrive in a fast-paced, highly collaborative environment, preferring end-to-end ownership over extreme specialization.

HFT Quantitative Developer

About DeepFin Research

DeepFin Research is a proprietary high-frequency trading (HFT) firm powered by cutting-edge Deep Learning (DL) and Deep Reinforcement Learning (DRL). We've brought on teammates from Nvidia, DeepMind, CitSec, Graviton, Tower, Jump, and others, and are aggressively working across cutting edge AI research and traditional quant research methods to monetise our AI generated signals across the global financial markets.

The Role

We are looking for exceptional quantitative developer with outstanding expertise at the intersection of quantitative research and C++ development to focus both on simulation and productionisation. This will include designing and building simulation across various financial markets, and taking high-frequency and mid-frequency alpha turning it into scalable, production-grade PnL across global exchanges.

Key Responsibilities

• Productionise research models into high-performance C++ systems used for live trading across multiple futures exchanges.
• Integrate and process L3/order-book data at scale, building and maintaining parsers, normalised book structures, and data pipelines.
• Develop and optimise execution logic, order placement strategies, queue-positioning logic, microstructure-aware order types, and latency-sensitive workflows.
• Collaborate closely with quant researchers to understand signals, model assumptions, and expected behaviours, ensuring seamless transition from prototypes into production code.
• Work with infra/engineering teams to deploy, monitor, and refine strategies in live environments; diagnose latency bottlenecks, instability, and performance drift.
• Build and maintain simulation/backtesting tools for microstructure-accurate evaluation of execution behaviour.
• Optional (desirable): support vol surface modelling, options execution logic, and cross-asset HFT monetisation.

Required Experience & Skills

• Must have Experience in HFT market making. 
• Outstanding C++ and Python expertise.
• Experience working with L3 and order-book data, market-data adapters, and high-throughput feed handling.
• Experience productionising HFT or mid-frequency strategies in live environments.
• Understanding of market microstructure: queue dynamics, adverse selection, order types, latency paths, tick-rule details.
• Ability to operate across both research and engineering disciplines: reading model code, understanding signals, and implementing robust production systems.
• Options experience is desirable
• FPGA experience is desirable

HFT Options and Volatility Trading Research

About DeepFin Research

DeepFin Research is a proprietary high-frequency trading (HFT) firm powered by cutting-edge Deep Learning (DL) and Deep Reinforcement Learning (DRL). We've brought on teammates from Nvidia, DeepMind, CitSec, Graviton, Tower, Jump, and others, and are aggressively working across cutting edge AI research and traditional quant research methods to monetise our AI generated signals across the global financial markets.

The Role

We are looking for exceptional high frequency trading researchers in the options space to monetise our AI driven signals in the global options markets, working in an exceptional team of options researchers.

Responsibilities

Volatility Surface & Pricing Models

• Design, implement, and calibrate ultra-fast vol surface models for equity and index options (e.g., SVI, SABR, Vanna-Volga).
• Integrate models into live trading systems for real-time fitting and quoting.
• Collaborate with quant devs to optimize model performance and stability across exchanges.

Market Making & Execution Research

• Develop and refine high-frequency quoting, hedging, and execution algorithms.
• Optimize order placement, queue position, and fill rates to reduce adverse selection and slippage.
• Strategy Development: Design and backtest systematic intraday strategies specifically targeting equity options, focusing on mean reversion, momentum, and premium decay.
• Analyze market microstructure and order-book dynamics to improve execution logic.

Realized Volatility & Signal Forecasting

• Build and enhance short-horizon realized volatility and spread forecasting models.
• Use high-frequency tick data to identify predictive microstructure and volatility patterns.

Risk & P&L Analytics

• Design real-time delta/gamma/vega hedging frameworks and risk dashboards.
• Dynamic Gamma Hedging: Build automated hedging frameworks to manage the non-linear risks of 0DTE portfolios, optimizing the trade-off between transaction costs and tracking error.
• Conduct PnL decomposition, tracking contributions from alpha, execution, and carry.
• Backtest strategies with realistic latency and cost models.

Ideal Candidate Profile

• Mandatory: Direct experience in high-frequency options trading - preferably market making on equity or index options.
• 5–7 years’ experience in a quant research or trading role at an HFT, prop firm, or leading options market maker.
• Deep understanding of options pricing, Greeks, and market microstructure.
• Experience with vol surface modeling (SVI, SABR, stochastic vol) and real-time model calibration.
• Proven background designing and testing execution logic and hedging systems in production.
• Strong programming ability in C++ and Python; experience with low-latency systems is a plus.
• Advanced degree (Master’s or PhD) in Mathematics, Physics, Statistics, Computer Science, or a related field.