ML Researcher, Foundational Models

Drive open-ended research on architecture, optimization, scaling behaviour, training stability, and post-training recipes for our next generation of foundational models.

Design and execute ablations at scales that actually inform large-run decisions — including running pre-training experiments end-to-end yourself.

Translate research findings into concrete proposals for the next training run, and own those proposals through to production.

Work shoulder-to-shoulder with the infrastructure and data teams; many of the most important research questions live at that boundary.

PhD in Machine Learning, Computer Science, or a closely related field (or in the final stages of completion).

First-author publications at top-tier ML venues such as NeurIPS, ICML, ICLR, ACL, EMNLP, or COLM.

Hands-on experience pre-training transformer-based language models from scratch, ideally at 7B+ parameters. You should be able to describe a training run you owned end-to-end…

Meaningful contributions to the open-source LLM ecosystem — research code, model releases, datasets, or substantive contributions to widely-used projects.