- 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.
- 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.
- Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment.
- 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).
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