Principal AI Researcher (Pre-Training Foundation Models)

Stabile Search•New York, NY

4d•Onsite

About The Position

My client is one of the most prestigious and reputable quantitative trading firms in the world and they are at the forefront of AI and Machine Learning investment and development. They are looking for an experienced ML Researcher Engineer who specializes in pre-training and building foundation Large Language Models. If you are excited about a role where you will have ownership over groundbreaking new projects in a fast-growing team, then this is the opportunity for you. As a ML Researcher in their new and fast-growing AI division, you will be developing and pre-training foundation Large Language Models (LLMs) that will power quantitative research analyzing market data across the firm.

Requirements

Experience pre-training and building foundation models.
Strong software engineering skills with a proven track record of building complex systems.
Expertise in Python and experience with deep learning frameworks.
Familiarity with large-scale machine learning, particularly in the context of language models.
Ability to balance research goals with practical engineering constraints.
Strong problem-solving skills and a results-oriented mindset.
Excellent communication skills and ability to work in a collaborative environment.
PhD or Masters in a related field.
Finance-related domain experience is not needed but interest in trading and finance is helpful.
Strong interest leveraging Machine Learning modeling to own and make a large impact in a quantitative finance setting.

Responsibilities

Conduct research and implement solutions in areas such as model architecture, algorithms, data processing, and optimizer development.
Rapidly implement the latest state-of-the-art methods from the deep learning literature.
Innovating new ideas for pre-training and new scaling paradigm.
Design, run, and analyze scientific experiments to advance their understanding of large language models.
Optimize and scale their training infrastructure to improve efficiency and reliability.