Quantitative Researcher (Middle / Senior)
We are seeking an exceptional quantitative researcher with expertise in reinforcement learning (RL) and quantitative modeling to develop on-chain trading strategies, focusing on ETH/USD+ and cbBTC/USD+ pairs.
Experience in trading is preferred but not required — we welcome candidates with strong machine learning, optimization, or mathematical modeling backgrounds who are eager to apply their expertise to algorithmic strategy development.
🔍 Key Responsibilities
Drive and execute quantitative research initiatives, designing and optimizing trading strategies for DeFi markets.
Develop and implement RL-based models, including Deep Q-Networks (DQN) and Avellaneda-Stoikov (AS) models for on-chain market making.
Define and refine state and action spaces for RL agents to optimize execution and risk management.
Design reward functions aligned with Overnight.fi’s profitability and risk control objectives, leveraging asymmetric dampened P&L frameworks.
Build and run rigorous backtesting and simulation frameworks to evaluate strategy performance.
Optimize model parameters using advanced techniques (e.g., genetic algorithms, Bayesian optimization).
Adapt strategies to high-latency, on-chain trading environments for robust execution and market efficiency.
Monitor, analyze, and continuously refine deployed models based on real-time data insights.
Stay current with trends in quantitative finance, RL, and DeFi to identify new opportunities.
🎓 Qualifications & Experience
PhD or Master’s degree in Quantitative Finance, Computer Science, Mathematics, Statistics, or related field (for middle/senior level).
Proven experience in quantitative research and reinforcement learning.
Deep knowledge of mathematical modeling, statistical analysis, and optimization.
Proficiency in Python and experience with frameworks such as TensorFlow, PyTorch, or JAX.
Experience in trading, market-making, or HFT is preferred but not required.
Familiarity with developing backtesting and simulation environments is a plus.
💡 Preferred Skills
Experience in state representation techniques for RL.
Familiarity with algorithm optimization methods (e.g., evolutionary algorithms, Bayesian approaches).
Knowledge of blockchain, smart contracts, and DeFi trading environments.
Experience working in high-performance computing (HPC) environments.
📁 Portfolio Submission (Preferred but Optional)
To strengthen your application, feel free to provide:
Code samples or GitHub projects showing quantitative models, RL applications, or backtesting frameworks.
Research papers, whitepapers, or technical articles in quantitative finance or algorithmic trading.
Trading strategy performance reports (e.g., backtesting results, live trading performance, market simulations).
💸 Compensation & Benefits
Competitive salary, negotiable based on experience
Performance-based incentives tied to strategy success
Flexible hybrid work model, tailored to you and your team
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