Aidan Allchin is an AI research scientist in San Francisco with nine years of engineering experience and an MS in Computational Data Analytics from Georgia Tech. He builds production-ready agentic systems and secure, containerized runtimes, with recent work focusing on long-horizon task benchmarks, RL policy optimization, and representation learning for large-scale systems data. His projects span self-hosted ambient AI assistants that orchestrate multi-device execution and certificate-based device authentication to cost-efficient, AI-driven ETL and migration pipelines that saved hundreds of thousands of dollars. Comfortable across the stack—from Postgres/RabbitMQ infrastructure and custom ORMs to PyTorch models and TypeScript frontends—he blends research rigor with pragmatic engineering. Notably, he engineered an Innovation Hub Predictor over 2.5M+ patents achieving strong predictive performance and pioneered agent handoff and large-context templating techniques to reduce inference costs.
9 years of coding experience
4 years of employment as a software developer
Master of Science - MS, Computational Data Analytics, 3.857, Master of Science - MS, Computational Data Analytics, 3.857 at Georgia Institute of Technology
Bachelor's degree, Economics, Bachelor's degree, Economics at Brown University
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