Senior Machine Learning Engineer

The Role

You'll join the small team building Bombadil and 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. This is a small team. You’ll drive machine learning projects from idea to production from end-to-end, rather than specializing into one piece of a much larger machine, and you’ll help build systems and practices that scale.

This is a senior, individually-contributing role with no direct reports, reporting to our Head of AI & ML.

What You'll Own

• 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.

What We're Looking For

• 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

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.

Compensation & logistics

• Salary range is $223k - $253k per year, with meaningful early-stage equity
• Standard benefits
• Full-time, in-person at our Presidio office in San Francisco
• Start date: ASAP