# Yutong Liu > Software Engineer at Uber Location: San Francisco, California, United States Profile: https://flows.cv/yutongliu • Experience in back end features and services implementation • Experience in data cleaning, data mining, and data visualization • Experience in multiple machine learning models development • Experience in giant company, unicorn, and early stage starups ## Work Experience ### Software Engineer (Intelligence Dispatch) @ Uber Jan 2019 – Present | San Francisco, CA Algorithm Optimization for Continuous Dynamic Driver-Rider Matching: • Develop scanning service to identify sub-optimal decisions for headroom analysis and possible better matching plan update. • Optimize dispatching mechanism by switching entity supplies injection strategy leading to 2% fulfillment rate increasing. Geo-Data Shadow Payload Environment Development for Staging Test: • Establish shadow environment by injecting dynamic percent replicated production traffic payload into shadow pod. • Intercept 97% production deployment issues by collecting code commits and streaming with shadow payload regularly. ### Software Engineer (Location / Motion) @ Apple Jan 2018 – Jan 2019 | Cupertino, CA Automation of Massive Spatiotemporal Data Enrichment and Visualization: • Enriched surrounding info based on spatiotemporal data from JSON format Google Earth KML files for following visualization. • Implemented screenshot feature to visualize aggregated target GPS data in Google Earth through keyboard/mouse simulation. Error Aggregation Summary with Integrated Verification Data Pipeline: • Integrated multiple verification data pipelines including sensor fusion, motion gestures, and fitness tracking to a single one. • Developed automated GPS statistics visualization modules for integrated Geo-error analysis report with 95% time saving. ### Software Engineer (Competence Center) @ Huawei Jan 2017 – Jan 2018 | Santa Clara, CA Simulated Cloud Deployment Optimization by Machine Learning Models: • Developed simulated Huawei Cloud modules and database schema based on hardware resources for deployment test. • Optimized system resources allocation via Gradient Boosting Tree and Random Forest models with 25% price reduction. ### Software Engineer (Data Process Division) @ E-Joule Technology Jan 2015 – Jan 2016 | Fremont, CA Testing Data Prediction of Recipe through Machine Learning Algorithms: • Predicted batteries properties based on recipe formula with Polynomial Regression methods for following experiments. • Validate materials composition with 90% error reduction via K-Means Clustering and K Nearest Neighbors approaches. ## Education ### Master's degree in Major in Computational Materials Science Washington University in St. Louis ### Bachelor's degree in Major in Materials Science Harbin Institute of Technology ## Contact & Social - LinkedIn: https://linkedin.com/in/musketeer-liu --- Source: https://flows.cv/yutongliu JSON Resume: https://flows.cv/yutongliu/resume.json Last updated: 2026-03-22