onsite
LLM Engineer
LLM Engineer
Trexquant Investment LP is looking for an LLM Engineer to develop, optimize, and deploy advanced machine learning models, specifically focusing on natural language processing (NLP) using large language models (LLMs). This role involves working with vast datasets, fine-tuning LLM systems for various applications like sentiment analysis and market predictions, and ensuring seamless integration into production environments to impact investment strategies.
About the role
About the Role
We are seeking an LLM Engineer to join our team. The successful candidate will play a critical role in developing, optimizing, and deploying advanced machine learning models, particularly focused on natural language processing (NLP) using large language models (LLMs). The role offers a unique opportunity to work on cutting-edge technologies and algorithms that directly impact our investment strategies.
Responsibilities
- Design, implement, and fine-tune systems incorporating large language models (LLMs) and other advanced artificial intelligence techniques for a variety of applications, including sentiment analysis, news aggregation, market predictions and data cleaning.
- Work with vast datasets, including structured and unstructured data, to train models that generate insights and forecasts critical to investment strategies.
- Continuously enhance the performance and efficiency of LLMs, ensuring that models are both scalable and resource-efficient.
- Partner with portfolio quant researchers to develop models that address specific market opportunities and challenges.
- Stay abreast of the latest NLP and LLM developments, contributing to internal thought leadership and pushing the envelope of what can be achieved.
- Deploy machine learning models in production environments, ensuring seamless integration with existing infrastructure and real-time market data feeds.
- Identify potential risks related to LLMs and ensure appropriate safeguards are in place, especially with regard to model bias and robustness.
Requirements
- Minimum 2 years of hands-on experience working with LLMs, NLP or deep learning in a high-performance environment.
- Experience working with large-scale datasets and deploying machine learning models in production.
- Knowledge of modern NLP techniques and frameworks (e.g., tokenizers, transformers, embedding models).
- Familiarity with machine learning platforms and tools (e.g., PyTorch, HuggingFace, OpenAI).
- Strong understanding of algorithmic trading and financial data is a plus.
- Excellent problem-solving abilities, with the capacity to translate complex business requirements into innovative technical solutions.