Ethan Joseph is a Machine Learning Engineer based in the San Francisco Bay Area with eight years of experience building research-driven ML systems and production features. He has moved models from research to impact—designing a recommender for a tax-professional marketplace, prototyping GPT-3–based NLG for ESG reporting, and improving Atari RL agents by over 200% in published research. His background blends strong academic results (BS summa cum laude from RPI, MEng at Cornell Tech) with hands-on systems work, from scalable GCP training pipelines to firmware verification tooling at Carnegie Mellon. He also led a 15+ developer open-source mobile/web project that navigated COVID-driven infrastructure changes, showing an ability to coordinate cross-functional teams under pressure. Known for squeezing efficiency out of training and evaluation (5x pipeline speedups) and for publishing novel NLP and data-to-text advances, he bridges academic rigor with product-focused delivery.
8 years of coding experience
2 years of employment as a software developer
Bachelor of Science - BS, Computer Science, 3.90/4.00 Summa Cum Laude, Bachelor of Science - BS, Computer Science, 3.90/4.00 Summa Cum Laude at Rensselaer Polytechnic Institute
Master of Engineering - MEng, Computer Science, Master of Engineering - MEng, Computer Science at Cornell Tech
Contributions:2 releases, 31 commits, 2 PRs in 3 years 4 months
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