Senior AI/Deep Learning Quantitative Researcher (Options & Futures) – Buy-Side Firm
at Durlston Partners
Posted 19 hours ago
No clicks
- Compensation
- Not specified
- City
- Not specified
- Country
- Not specified
Currency: Not specified
Join a buy-side quantitative research team focused on AI-driven systematic trading across options, futures, and structured derivatives. You will develop state-of-the-art deep learning and reinforcement learning models for real-time alpha generation, portfolio optimization, and execution strategies. The role includes building NLP models on alternative datasets, deploying models to production with real-time inference, and improving research infrastructure and backtesting. Collaboration with portfolio managers and execution teams to integrate risk-managed, AI-driven signals is a core responsibility.
About Our Client
Our client is a rapidly expanding buy-side firm specializing in systematic trading across derivatives and cash markets. With a strong emphasis on AI-driven research, they integrate deep learning and reinforcement learning into high-frequency and medium-frequency trading strategies to generate uncorrelated returns for institutional investors.
The Opportunity
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.
Key Responsibilities
- Develop advanced AI/Deep Learning models for predictive signal generation in derivatives markets (volatility surfaces, term structure forecasting, 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 (news sentiment, earnings call transcripts, 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.
Ideal Candidate Profile
- 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, TensorFlow, 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, AlphaZero-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.
Why Join?
- Be a key player in an AI-first quantitative research team, working with top-tier hedge fund PMs and AI researchers.
- Work with exclusive datasets (tick-level options data, alternative data partnerships, deep order book data).
- Access to world-class compute resources for deep learning model training and high-performance AI-driven research.
- Remote-first culture with flexibility to work from anywhere, while engaging with top-tier talent in AI and systematic trading.
Senior AI/Deep Learning Quantitative Researcher (Options & Futures) – Buy-Side Firm
at Durlston Partners
Senior AI/Deep Learning Quantitative Researcher (Options & Futures) – Buy-Side Firm
at Durlston Partners
Posted 19 hours ago
No clicks
- Compensation
- Not specified
- City
- Not specified
- Country
- Not specified
Currency: Not specified
Join a buy-side quantitative research team focused on AI-driven systematic trading across options, futures, and structured derivatives. You will develop state-of-the-art deep learning and reinforcement learning models for real-time alpha generation, portfolio optimization, and execution strategies. The role includes building NLP models on alternative datasets, deploying models to production with real-time inference, and improving research infrastructure and backtesting. Collaboration with portfolio managers and execution teams to integrate risk-managed, AI-driven signals is a core responsibility.
About Our Client
Our client is a rapidly expanding buy-side firm specializing in systematic trading across derivatives and cash markets. With a strong emphasis on AI-driven research, they integrate deep learning and reinforcement learning into high-frequency and medium-frequency trading strategies to generate uncorrelated returns for institutional investors.
The Opportunity
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.
Key Responsibilities
- Develop advanced AI/Deep Learning models for predictive signal generation in derivatives markets (volatility surfaces, term structure forecasting, 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 (news sentiment, earnings call transcripts, 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.
Ideal Candidate Profile
- 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, TensorFlow, 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, AlphaZero-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.
Why Join?
- Be a key player in an AI-first quantitative research team, working with top-tier hedge fund PMs and AI researchers.
- Work with exclusive datasets (tick-level options data, alternative data partnerships, deep order book data).
- Access to world-class compute resources for deep learning model training and high-performance AI-driven research.
- Remote-first culture with flexibility to work from anywhere, while engaging with top-tier talent in AI and systematic trading.
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