Summary
Ryan Slattery is a deep learning-focused machine learning engineer with roughly a decade of experience applying data science to real-world problems, from synthetic data generation for fish biomass estimation to fine-tuning LLMs and vision models. With a B.S. in Molecular, Cell, and Developmental Biology and an advanced ML specialization from Springboard, he uniquely blends biological insight with technical expertise in Python, PyTorch, NLP, and model fine-tuning. Recent personal projects demonstrate practical ingenuity—forcing LLMs to emit schema-valid JSON and achieving >99% accuracy fine-tuning LLMs on IMDB—highlighting strong prompt engineering and experimentation skills without resorting to full-scale retraining. He’s contributed production-ready work at companies like Launch by NTT DATA and Sephora and stays current by reading papers and building exploratory projects that bridge biologically inspired theories (e.g., HTM, Tolman-Eichenbaum) with modern unsupervised methods. Outside of work he pursues curiosity-driven learning, often expressing it through GitHub prototypes that test unconventional ideas in model behavior and training.
10 years of coding experience
4 years of employment as a software developer
University of California Santa Cruz
Data Science Career Track - Advanced Machine Learning Specialization, Data Science Career Track - Advanced Machine Learning Specialization at Springboard