HFT Quantitative Researcher

Deep Fin 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.

Deep Fin 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, Deep Mind, Cit Sec, 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.

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.

• Design, implement, and calibrate ultra-fast vol surface models for equity and index options (e.g., SVI, SA , 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.

• 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.

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

• 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 0
  
  DTE 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.

• Mandatory:
  Direct experience in high-frequency options trading - preferable 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, SA , 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.

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. Deep Fin offers a collaborative, research-driven environment where ideas move quickly from concept to execution and where every contribution has visible impact.

Join us in building the next generation of deep-learning-driven trading systems - shaping the future of finance through innovation, rigour, and technology.