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Quantitative LLM Strategy Lead
2 months ago
We are seeking an experienced Large Language Model (LLM) specialist to lead and grow the LLM Quantitative Strategy Team at Trexquant Investment. As a key member of our team, you will drive the development of advanced machine learning models to create and develop trading strategies.
Key Responsibilities- Lead and develop a team of researchers in researching, implementing, and trading profitable LLM-based quantitative strategies.
- Stay updated on the latest LLM research and integrate it into signal development and our general investment process.
- Stay abreast of the latest advancements in machine learning, natural language processing, and quantitative finance, and apply cutting-edge techniques to enhance Trexquant's investment capabilities.
- Identify datasets useful for building LLM trading strategies and build pipelines to feed these data into our research and trading platforms.
- Conduct research and experimentation to develop novel machine learning models for quantitative trading strategies across various asset classes and time horizons.
- Work with the development team to improve accuracy, robustness, and speed of our platform in simulating and trading LLM-based strategies.
- Bachelor's, Master's, or Ph.D. degrees in machine learning, Computer Science, statistical modeling, or other related STEM fields.
- 4+ years of experience in machine learning research and development, with a focus on large language models and high-volume, data-intensive applications.
- Strong proficiency in programming languages such as Python, and experience with machine learning libraries/frameworks such as TensorFlow, PyTorch, or Hugging Face Transformers.
- Demonstrated track record of developing and implementing machine learning models in real-world applications, preferably in the context of quantitative trading or algorithmic trading.
- Experience managing and growing a team of quant researchers.
- Competitive salary, plus bonus based on individual and company performance.
- Collaborative, casual, and friendly work environment.