Quantitative ML 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

Skills Required

Airflow, Kafka, Redis, Tensorflow, Numpy, Pandas, Pytorch, Docker, Kubernetes, Python