Position Overview
Syncretic Research LLC is a machine learning research and development firm specializing in world model engineering, human-aligned AI systems, multi-agent coordination, and research-to-production translation. We bridge the gap between cutting-edge ML research and practical implementation across finance, healthcare, geospatial intelligence, and other complex domains. We're seeking a PhD-level Quantitative Systematic Trader to develop and implement sophisticated trading strategies leveraging advanced machine learning techniques. This role combines rigorous quantitative research with production system development, requiring someone who can translate theoretical insights into robust, profitable trading systems.
Key Responsibilities
- Design, develop, and deploy systematic trading strategies using machine learning and statistical methods
- Build and maintain quantitative models for alpha generation, risk management, and portfolio optimization
- Conduct rigorous research on market microstructure, price formation, and predictive signals
- Implement production-grade trading systems with appropriate monitoring, risk controls, and execution logic
- Translate scientific research into actionable trading insights while maintaining scientific rigor
Required Qualifications
- PhD in Computer Science, Statistics, Mathematics, Physics, Economics, Electrical Engineering, or a related quantitative field
- Deep understanding of machine learning theory and practice (neural networks, reinforcement learning, Bayesian methods, etc.)
- Strong programming skills in Python; experience with modern ML frameworks (PyTorch, JAX, TensorFlow)
- Solid foundation in probability theory, stochastic calculus, and statistical inference
- Demonstrated ability to conduct independent research and translate findings into working systems
- Experience with time series analysis and forecasting methods
Preferred Qualifications
- Prior quantitative trading or financial modeling experience
- Publications in top-tier ML/AI conferences or quantitative finance journals
- Experience with graph neural networks, temporal models, or multi-agent systems
- Familiarity with market microstructure and order book dynamics
- Track record of deploying research systems into production environments
- Background in reinforcement learning or imitation learning applications