Design methods, tools, and infrastructure to push forward the state of the art in large language models.

Define research goals informed by practical engineering concerns.

Contribute to experiments, including designing experimental details, writing reusable code, running evaluations, and organizing results.

Adapt standard machine learning methods to best exploit modern parallel environments (e.g. distributed clusters, multicore SMP, and GPU).

Work with a large and globally distributed team.

Contribute to publications and open-sourcing efforts.

Minimum Qualifications

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience.

Research experience in machine learning, deep learning, and/or natural language processing.

Experience with developing machine learning models at scale from inception to business impact.

Programming experience in Python and hands-on experience with frameworks such as PyTorch.

Exposure to architectural patterns of large scale software applications.

Preferred Qualifications

Master's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience.

A PhD in AI, computer science, data science, or related technical fields.

Direct experience in generative AI and LLM research.

First author publications at peer-reviewed AI conferences (e.g., NeurIPS, CVPR, ICML, ICLR, ICCV, and ACL).