About

I’m Ethan Tenison, a machine learning engineer interested in building systems for problems where uncertainty is real, stakes matter, and clean answers are rare. I’m especially drawn to work shaped by Bayesian thinking, decision-making under uncertainty, and the use of domain knowledge to build systems that reflect the structure of the world they operate in.

My work spans production ML, retrieval-based systems, optimization, and applied AI. Across those areas, I care less about novelty for its own sake than about whether a system is reliable, interpretable, and actually useful in practice. I’m generally skeptical of generic AI approaches that ignore the assumptions, constraints, and prior knowledge that make a problem what it is.

I like work that begins messy and becomes clearer through modeling, iteration, and engineering. In practice, that means building tools that do more than generate outputs, they help people reason, decide, and act with more confidence.

Right now, I’m focused on senior ML and AI roles where I can build robust, decision-oriented systems.