AI Trading Systems Engineer

As a Quantitative Machine Learning Engineer at Merli , you will help shape the next generation of AI-driven trading infrastructure . This role sits at the intersection of quantitative research, applied ML, and agentic system design , with the goal of optimizing high-frequency (HFT), medium-frequency (MFT), and wholesale trading strategies.

You’ll architect adaptive, agent-based ML systems that learn from evolving market microstructures, build robust forecasting and optimization models, and work with trading and infrastructure teams to deploy these solutions in real-world, low-latency environments .

Key Responsibilities

• Design & Build Agentic ML Systems : Develop autonomous and semi-autonomous agents that perform data acquisition, alpha discovery, backtesting, and execution optimization.
• End-to-End ML Engineering : Architect, train, and deploy ML pipelines for HFT / MFT and wholesale trading, from signal generation to execution integration.
• Quantitative Research Integration : Collaborate with quant researchers to translate theoretical models into production-ready predictive and optimization systems.
• Market Forecasting : Develop deep learning, time series, and reinforcement learning models for price movement prediction and regime detection.
• Trading Optimization : Build reinforcement and meta-learning frameworks that adaptively tune strategy parameters in live environments.
• Scalable ML Infrastructure : Implement real-time inference, model versioning, and continuous learning pipelines for production systems.
• Performance Evaluation : Rigorously validate models with historical and synthetic simulations, ensuring robustness, latency, and financial soundness.
• Documentation & Collaboration : Maintain high standards of reproducibility, version control, and code documentation across research and deployment layers.

What You’ll Gain

Qualifications

Preferred Technical Stack