Summary
What you’ll impact
The senior ML engineer will own the end-to-end development of the organization’s transformer model, including data curation, architecture design, training, evaluation, and production lifecycle. They will drive projects from idea through deployment, building scalable systems and practices for a small, fast‑moving team.
Responsibilities
What you'll do
- Work directly on training: the data the model learns from, the architecture and training decisions that determine whether a run succeeds, and the evaluation work to measure our progress.
- Drive machine learning projects from idea to production from end-to-end.
- Build systems and practices that scale.
- Transformer training. Model architecture, data curation, training strategies, and the full lifecycle to production.
- Evaluation of the core model. Building and refining evaluations to measure progress of new model capabilities.
- Model lifecycle practices. Develop robust, repeatable model lifecycle systems for rapid iteration.
Requirements
What you’ll bring
- 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.
- Experience with biological, environmental, or other scientific data.