ML Research Engineer

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.
  
  

• 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