ML Research Engineer
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.
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
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