• Has built and trained transformer models from scratch, not only fine-tuned them, and can speak concretely to the data, architecture, and training-stability decisions involved.
• Roughly 3+ years of hands-on transformer-specific experience is ideal, generally within 5-10 years of overall ML/engineering experience.
• Has shipped or maintained ML systems in a practical capacity.
• Comfortable driving projects end-to-end, from data collection through evaluation, without a large specialized team around you.
• Strong software engineering fundamentals: you build and maintain real training and evaluation infrastructure, not just notebooks.
• Technical or scientific depth, not necessarily in CS or ML specifically.
• Comfortable being one of a very small number of people responsible for a company's core model.
• Strong plus
• Experience with biological, environmental, or other scientific data.
• What This Isn't
• What This Isn't
• This isn't a role for someone who wants a large ML org's infrastructure and specialization already built around them; on a team this size, you'll cover more of the pipeline yourself. It's also not a pure research role: the work has to end up in a model that ships, not just a result that publishes. If you want to bring new skills to a small team building something new, and to build the practice as much as the model, we'd like to talk.