Head of LLM Quantitative Strategy Team (USA)

Trexquant Investment • Stamford, CT, United States • $200k - $250k / year • 1m ago

We are looking for an experienced large language model (LLM) specialist to lead and grow the LLM Quantitative Strategy Team at Trexquant. In this role, you will drive the development of advanced machine learning models to create and develop trading strategies. We are looking for someone who demonstrates deep expertise in large language models and strong ability to apply machine learning methodologies to complex, high-volume datasets.

Responsibilities:

Lead and develop a team of researchers in researching, implementing, and trading profitable LLM based quantitative strategies.
Continuously stay updated on the latest LLM research and integrate it into signal development and into 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. 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.

Requirements:

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.

#J-18808-Ljbffr