# Ryan Tan > ML Engineer, CS @ Stanford | Mechanistic Interpretability Research Location: San Francisco Bay Area, United States Profile: https://flows.cv/ryantan CS Coterm at Stanford, SW/MLE and Independent Mechanistic Interpretability researcher. Come see what I’m up to - technical blog: https://amagibaba.com ## Work Experience ### Software Engineer @ Glean Jan 2025 – Present | Palo Alto, California, United States Software Engineer, but More Expensive(TM). ### Software Engineer @ Glean Jan 2024 – Jan 2025 | Palo Alto, California, United States On the Platforms Infrastructure team, building robust and scalable distributed infrastructure on which all of glean runs ### Researcher - Stanford Vision & Learning Lab @ Stanford Artificial Intelligence Laboratory (SAIL) Jan 2023 – Jan 2024 | Stanford, California, United States Led a Brain-Robot-Interface project through the computational neuroscience phase, iterating on deep and classical statistical methods to classify EEG data collected from human subjects imagining movement (motor imagery) with high accuracy (45% -> 85%, 4-way) allowing humans to interface with robots to perform general day-to-day tasks (e.g. household chores). Under the supervision of Ruohan Zhang and Feifei-Li. CoRL 2023. ### Machine Learning Engineer @ Hive Jan 2023 – Jan 2024 | San Francisco, California, United States - Built the company's first AI-generated audio detector: https://docs.thehive.ai/docs/ai-generated-audio-detection - Distributed training of large multi-head image classifiers to perform at super-high accuracy - Built data sourcing / filtering tools (embedding / vector search, automatic image captioning, efficient sampling, etc.) ### Research Assistant - Laboratory of Behavioral and Cognitive Neuroscience (LBCN) @ Stanford University School of Medicine Jan 2022 – Jan 2023 | Stanford, California, United States Using deep learning to model EEG data to predict various types of pathological behavior for the goal of developing therapies for such pathologies. ### Student Museum Guide @ Anderson Art Collection Jan 2021 – Jan 2023 | Stanford, California, United States ### Systems Design, Software Engineering Intern @ Chicago Trading Company Jan 2022 – Jan 2022 | Chicago, Illinois, United States Built tools to triage data pipeline performance issues, reducing problem detection and resolution time from hours / days to minutes. Improved market data ingestion systems by reducing memory requirements to a 1000th of the original without any speed performance impact. Completed all quant intern offerings including a basic Options Theory class, mock trading, and quantitative modeling. ### Machine Learning Research Intern @ Oracle Jan 2021 – Jan 2021 | Redwood City, California, United States Worked on Parallel Graph AnalytiX (PGX), building explainable graph neural networks for sub-graph classification. Worked with small dataset of synthetic financial fraud data, and helped develop a model that achieved results on par with the state-of-the-art GCN (Graph Convolutional Network) and MAGNN (meta-path aggregating GNN). Built GNN backtesting pipelines and visualizer of top contributors to sub-graph classification results. ### Machine Learning Research Intern @ DSTA Jan 2020 – Jan 2020 | Singapore Designed and built a model for anomaly detection on aggregated time-series data (traffic paths), used in real time operations. Researched on GPS and neural pathway clustering, and built a final model that combined deep-learning models (Long-Short-Term-Memory Neural Networks or LSTMs) with Threshold-based Hierarchical Clustering, achieving a true-positive rate of 95% and a false- positive rate of 15%. Done using Python, and deployed onto a Docker image; to be used real-time on real, sensitive data. ### Chief Technology Officer @ Advisory Singapore Jan 2019 – Jan 2020 Leading a young and motivated team of coders to plug the technology gap of web development and web analytics in Advisory Singapore - a fully youth-led non-profit dedicated to empowering young students to make informed career and higher education choices. ### Events Manager @ Advisory Singapore Jan 2018 – Jan 2019 | Singapore As Advisory's first Events Manager, organized and facilitated a series of four public events - wherein working professionals from various sectors and industries were invited to engage in panel discussions, breakout sessions, and networking sessions with high-school students for an afternoon. ### Correspondent @ Advisory Singapore Jan 2017 – Jan 2018 | Singapore Organized and conducted interviews with various industry professionals from all walks of life - Science & Tech, Education, Environmental Protection, Career Design, Entrepreneurship, etc. - and published written articles on their occupational lives (www.advisory.sg). ### Machine Learning Research Intern @ DSO National Laboratories Jan 2019 – Jan 2019 | Singapore Researched on and built software on Computer Vision - in end-to-end object detection & recognition, and text detection & recognition. Reviewed a mix of classical and deep learning (DL) techniques in recent literature. Created a graphical tool in C# and C++ with DL (YOLO) and classical capabilities to allow automatic image annotation and editing. Did literature review on existing text detection and (separate) recognition models and to propose possible integrated end-to-end model. ## Education ### Master of Science - MS in Computer Science Stanford University ### Bachelor's degree in Computer Science Stanford University ### High School Diploma in Singapore-Cambridge A Levels Hwa Chong Institution ## Contact & Social - LinkedIn: https://linkedin.com/in/ryantjj - Portfolio: https://amagibaba.com --- Source: https://flows.cv/ryantan JSON Resume: https://flows.cv/ryantan/resume.json Last updated: 2026-04-11