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# Machine Learning Quantitative Researcher

- **Company:** Evolve Inc
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Salary:** $200,000 - $300,000
- **Contract:** Permanent contract
- **Skills:** Big Data, C++ (Programming Language), Python (Programming Language), Machine Learning, Natural Language Processing, Reinforcement Learning, Graph Neural Networks, Deep Learning, Information Technology, Machine Learning Operations
- **Published:** November 8, 2026
- **Apply:** https://www.juju.com/job/28_25304_f6d75415a947e2386c81abfbb2a85245

## About the Role

* Minimum 3 years of experience applying machine learning in systematic trading, alpha signal generation, or predictive modeling.
 * Advanced degree (PhD or Master's) in a quantitative or technical discipline (statistics, mathematics, computer science, engineering, physics).
 * Strong programming expertise in Python and C++, with proven ability to productionize models.
 * Experience with transformers, reinforcement learning, or GNNs applied to time series/financial data.
 * Solid understanding of market microstructure and intraday trading dynamics.
 * Excellent problem-solving skills and the ability to thrive in a collaborative, high-performance environment.

## Description

A leading proprietary trading firm is seeking a Machine Learning Quantitative Researcher to join its systematic trading team. In this role, you'll design and deploy advanced machine learning models - from NLP-driven signals to graph-based time series models - with the goal of generating alpha in global equities and futures markets., * Research, design, and implement novel trading signals using machine learning, NLP, and deep learning.

 * Conduct large-scale data analysis, backtesting, and scenario simulations to validate robustness and stability.
 * Build production-grade ML pipelines, including retraining, drift detection, and real-time monitoring.
 * Apply advanced architectures such as transformers, attention models, and graph neural networks to financial time series and cross-asset datasets.
 * Collaborate with engineering and trading teams to optimize infrastructure and ensure seamless execution.