# Patrick Kao > Software Engineer at Wing Location: San Francisco Bay Area, United States Profile: https://flows.cv/patrickkao I am a Software Engineer at Wing and an MIT graduate (B.S./M.Eng) driven by a lifelong fascination with autonomous systems and AI. My work focuses on the intersection of software infrastructure, robotics, and deep learning. At Wing, I’ve focused on engineering the UAS traffic management backend and optimizing geospatial data pipelines, achieving significant reductions in latency and compute resource costs. Beyond core backend development, I’ve co-led the "Wing AI Champions" initiative, helping scale AI adoption across our engineering teams through developer guides and live workshops. My technical foundation is rooted in AI research and rigorous testing. At the MIT Distributed Robotics Lab, I co-authored research on "Robust flight navigation out of distribution with liquid neural networks", published in Science Robotics. From building CI/CD pipelines at NVIDIA to optimizing CNN video feature aggregation at Meta, I enjoy the challenge of making autonomous systems more efficient, modular, and scalable. Whether I’m competing in hackathons or developing personal projects, I love creating practical solutions that have a tangible impact on the community. Check out my website at https://www.patrickkao.net ## Work Experience ### Software Engineer @ Wing Jan 2022 – Present | Palo Alto, CA - Rearchitected safety-critical UAV landing logic by implementing C++ backend microservice handlers and transactional queues, achieving 70% reduction in resource cost while ensuring system reliability - Optimized geospatial data importer performance, achieving a 50% reduction in latency and a 95% decrease in compute resources through new push-based, modular framework - Co-led "Wing AI Champions" to accelerate adoption, driving AI usage to 88% by authoring developer guides and teaching live workshops - Managed software engineer vendor to modernize legacy infrastructure ### Undergraduate Researcher @ MIT Distributed Robotics Laboratory, CSAIL Jan 2019 – Jan 2022 | Cambridge, MA - Published "Robust flight navigation out of distribution with liquid neural networks" in Science Robotics (2023), garnering 100+ citations and recognition in Popular Science article - Compared neural architectures on generative drone flight navigation task by augmenting training data, implementing networks in TensorFlow, and training networks with TPE-based hyperparameter optimization - Engineered a ROS onboard control system to conduct experiments and validate model performance in real-world flight scenarios - Implemented DESIRE trajectory prediction deep neural network in PyTorch to provide lab baseline accuracy metric - Developed Jenkins integration test framework which ran autonomous vehicle perception and control software in simulation using Docker ### Software Intern @ Meta Jan 2021 – Jan 2021 | Menlo Park, CA - Optimized CNN video feature aggregation strategy to enable large-scale copy detection and free over 1000 machines used for legacy system - Curated datasets with SQL and used Pandas to preprocess datasets for Pytorch models ### Software Intern @ Microsoft Jan 2021 – Jan 2021 | Menlo Park, CA - Wrote XCTest UI tests to verify Intune MAM policy behaviors - Integrated UI tests into Azure Pipelines CI/CD system, automating regression detection ### Software Intern @ NVIDIA Jan 2020 – Jan 2020 | Santa Clara, California, United States -Converted existing CMake build framework to Bazel for autonomous vehicle platform code, improving modularity and decreasing build times for downstream customers -Wrote Bazel packaging rules and integrated them into CI/CD pipeline, automating packaging and enabling code delivery ### Software Intern @ Microsoft Jan 2020 – Jan 2020 | Cambridge, Massachusetts, United States -Automated building and Docker containerization of Windows to Linux application translation tool -Integrated automatic tool into downstream CI/CD pipelines, eliminating manual upkeep requirement, improving developer efficiency, and increasing build reliability for customer-facing applications ### Software Intern @ Foresight AI Inc Jan 2019 – Jan 2019 | San Jose, CA -Developed machine-learning-based trajectory maneuver classification system and applied it to organize company’s entire data corpus -Automated existing data generation pipeline with Airflow and pioneered Jenkins regression test system ### Undergraduate Researcher @ MIT Design Lab Jan 2018 – Jan 2019 | Cambridge, MA -Worked on sponsored project to design and prototype drones that function in extreme environments -Implemented control, navigation, visual odometry, and sensor processing software ### Research Intern @ Foresight AI Inc Jan 2019 – Jan 2019 | San Jose, CA -Engineered mobile robots capable of automating company map data collection -Designed hardware configuration and programmed software stack capable of autonomous control, localization, and object recognition ### Research Intern @ Stanford University Autonomous Systems Lab Jan 2017 – Jan 2018 | Stanford, CA -Defined specifications and built hardware for a quadcopter capable of carrying heavy loads -Evaluated the suitability of visual odometry and Simultaneous Localization and Mapping (SLAM) software packages in C++ and Python for tracking the position of a quadcopter using an onboard camera -Presented work to Professor Marco Pavone's ASL Lab and AA 290 masters students -Acknowledged in research paper, ”Perception-Aware Motion Planning via Multiobjective Search on GPUs”, that was presented at the International Symposium on Robotics Research by Stanford ASL members ### Volunteer Mentor @ Stanford UAV Club Jan 2015 – Jan 2018 | Stanford, CA -Mentored SUAVE 101 course and taught Stanford students how to build hardware and configure software to fly RC quadcopters and airplanes -Prepared new prototype aircraft for SUAVE 101 course and wrote instruction manual for students -Invited as a SUAVE presenter at the 2016 Stanford Aeronautics & Astronautics School of Engineering Affiliates Day Poster Session ### Project Member @ Stanford UAV Club 20G Project Jan 2015 – Jan 2017 | Stanford, CA -Collaborated with team to try to build the world's most powerful quadcopter and break the world record for fastest climb to 100 meters (Guinness World Records application in progress) -Sole programmer and wrote the flight control software in C++ to stabilize high power quadcopter -Independent work on flight control software won Finalist and 1st place special award from the Society of Experimental Test Pilots at Intel International Science and Engineering Fair (ISEF) 2016 ## Education ### Master of Science - MS in Computer Science Massachusetts Institute of Technology ### Bachelor of Science - BS in Computer Science Massachusetts Institute of Technology ### Menlo-Atherton High School ## Contact & Social - LinkedIn: https://linkedin.com/in/patrick-kao - Portfolio: https://patrickkao.net --- Source: https://flows.cv/patrickkao JSON Resume: https://flows.cv/patrickkao/resume.json Last updated: 2026-03-31