TJ Bai is an experienced engineer currently working at Auctor since May 2025. Previously, TJ held a research position at The Johns Hopkins University from January 2023 to July 2025, where contributions included authoring a TACL paper on efficient multi-agent inference alongside Jason Eisner and exploring various architecture and post-training concepts. Additionally, TJ served as a Teaching Assistant for a Probability course and gained engineering experience with Apple and Amazon Web Services (AWS) in 2024 and 2023, respectively. TJ holds a Bachelor of Science in Computer Science and Applied Mathematics from The Johns Hopkins University.
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