Summary
Nicholas Fleischhauer is a founding engineer and machine learning practitioner with a decade of experience building end-to-end AI systems that prioritize interpretability, causal insight, and production resilience. He combines full‑stack engineering chops with advanced causal inference and model-interpretability tooling to move beyond average A/B results toward targeted, heterogeneous treatment effects that drive real business value. Nicholas has architected event-driven backends and hierarchical Bayesian online learners, implemented hallucination detection in generation pipelines, and automated workflows that reduce manual toil at scale. Comfortable across GPU model stacks, vector embeddings, and cloud infrastructure, he bridges research-grade methods and pragmatic deployment. Outside work he obsessively automates everyday tasks—a hint of the tinkerer mindset that informs his efficiency-first approach to product engineering.
10 years of coding experience
5 years of employment as a software developer
Bachelor's Degree Data Science & Applied Math, Bachelor's Degree Data Science & Applied Math at University of California, Berkeley
Master's degree Data Science, Master's degree Data Science at University of San Francisco
The Job Hackers
Hack Reactor
Skyline College
Full Stack Web Developer, Full Stack Web Developer at UC Berkeley Extension