ML Research Engineer

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Below, you will find a complete breakdown of everything required of potential candidates, as well as how to apply Good luck.Key Responsibilities:

Develop and implement state-of-the-art machine learning models for trading and investment strategies.Research and apply techniques such as deep learning, reinforcement learning, and NLP to large-scale financial datasets.Optimize and scale ML pipelines for real-time and batch processing.Collaborate with quantitative researchers and portfolio managers to translate research into production-grade models.Explore alternative data sources and feature engineering techniques to enhance predictive power.Contribute to the development of proprietary ML infrastructure and tooling.Requirements:

Advanced degree (MSc/PhD) in Machine Learning, Computer Science, Statistics, Applied Mathematics, or a related field.Strong experience in designing and implementing ML models, particularly in time-series forecasting, NLP, or deep learning.Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, or JAX.Solid understanding of probability, statistics, and optimization.Experience with large-scale data processing frameworks (e.g., Spark, Dask, Ray) is a plus.Prior exposure to financial markets, trading, or quantitative research is essential.Seniority level Entry levelEmployment type Full-timeJob function Research

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