Research, Pre-Training Science

Ability to design, run, and analyze experiments thoughtfully, with demonstrated research judgment and empirical rigor.

Experience with distributed or high-performance computing environments.

Proficiency in Python and familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow, or JAX). Comfortable with debugging distributed training and writing code that scales.

Bachelor’s degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding.

Clarity in communication, an ability to explain complex technical concepts in writing.

The role of pre-training researchers sits at the core of our roadmap. This work advances the science of how large models learn from data. You’ll explore new pre-training methods, architectures, and learning objectives that make model training efficient, robust, and aligned with human goals.

This role blends fundamental research and practical engineering, as we do not distinguish between the two roles internally. You will be expected to write high-performance code and read technical reports. It’s an excellent fit for someone who enjoys both deep theoretical exploration and hands-on experimentation, and who wants to shape the foundations of how AI learns.

Note: This is an "evergreen role" that we keep open on an on-going basis to express interest in this research area. We receive many applications, and there may not always be an immediate role that aligns perfectly with your experience and skills. Still, we encourage you to apply. We continuously review applications and reach out to applicants as new opportunities open. You are welcome to reapply if you get more experience, but please avoid applying more than once every 6 months. You may also find that we put up postings for singular roles for separate, project or team specific needs. In those cases, you're welcome to apply directly in addition to an evergreen role.

Research and develop new methodologies for pre-training.

Work in areas such as scaling, architecture, algorithms, or optimization of large scale training runs depending on your research interest and experience.