Senior Machine Learning Engineer
About The Position
The Electric Plant Co. is building a new category of plant intelligence. Our Model, Bombadil, decodes those signals into real-time insights about plant health, growth, and stress. Our IoT hardware measures the hidden electrical signals in plants and trees, paired with environmental data, and our foundation model. We're a small, fast-moving company working at the intersection of biology, hardware, AI, and IoT. 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.
Requirements
- 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.
Nice To Haves
- Experience with biological, environmental, or other scientific data.
Responsibilities
- 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.
Benefits
- Meaningful early-stage equity
- Standard benefits
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
