ML Researcher, Foundational Models

About the role

You'll work on the open-ended questions that decide what our next models look like. This is not a fine-tuning role. It's not an "apply known recipes" role. We're looking for a researcher who can take a vague hunch about architecture, optimization, data composition, or training dynamics, design the right ablations, run them at meaningful scale, and turn the result into a decision that ships in a production training run.

What you'll do

• 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
• Read broadly, write internally, and publish externally when the work merits it

What you need

• 3–6 yrs of experience
• Machine learning
• LLMs
• PyTorch
• ETL
• PhD