Senior AI​/Deep Learning Quantitative Researcher

Job Overview

Join an elite research team at the forefront of AI‑driven quantitative trading. This role focuses on applying state‑of‑the‑art deep learning techniques to options, futures, and structured derivatives markets with direct exposure to real‑time alpha generation, portfolio optimization, and execution strategies. You will develop cutting‑edge models for forecasting market dynamics and optimizing trade execution in a systematic data‑driven framework.

• Develop advanced AI deep learning models for predictive signal generation in derivatives markets, volatility surfaces, term‑structure forecasting, and order flow dynamics.
• Apply reinforcement learning to optimize execution strategies, market making, and hedging frameworks.
• Build and refine NLP‑based models for extracting signals from alternative datasets such as news sentiment, earnings call transcripts, and options order flow.
• Enhance systematic options trading strategies (e.g., delta‑hedging, volatility arbitrage, statistical arbitrage) using deep learning‑based predictive frameworks.
• Deploy and optimize AI models in production with real‑time inference and model adaptation to changing market conditions.
• Improve research infrastructure—scalable data pipelines, high‑performance backtesting engines, deep learning model training frameworks.
• Collaborate with portfolio managers and execution teams to integrate AI‑driven signals into risk‑managed trading portfolios.
• Publish internal research on deep learning architectures for financial time series forecasting, reinforcement learning for derivatives trading, and explainability of AI‑driven strategies.

• 5+ years of experience in quantitative research, systematic trading, or AI‑driven signal development at a top‑tier hedge fund, prop firm, or high‑frequency trading firm.
• Strong track record of alpha generation in derivatives markets (Sharpe ratio, risk‑adjusted returns, and execution efficiency).
• Expertise in AI/Deep Learning frameworks:
  PyTorch, Tensor Flow, JAX, or Hugging Face Transformers.
• Strong programming skills in Python and C++ (for low‑latency research and execution).
• Advanced degree (PhD preferred) in AI, Machine Learning, Quantitative Finance, or Computational Sciences.
• Deep knowledge of neural networks for time‑series forecasting (LSTMs, Transformer models, CNNs for market data).
• Reinforcement learning for execution optimization (Q‑learning, PPO, Alpha Zero‑style models).
• Generative models for synthetic data generation (GANs, VAEs, diffusion models).
• Derivatives pricing models (stochastic volatility, Monte Carlo simulations, local volatility).
• Market microstructure and high‑frequency trading for listed options and futures.
• Portfolio optimization under liquidity, margin, and regulatory constraints.