Gene Der Su
About Gene Der Su
Gene Der Su is a Software Engineer currently working at Anyscale in San Francisco, California. He has extensive experience in machine learning and software engineering, having previously worked at Amazon and other notable organizations.
Current Role at Anyscale
Gene Der Su is currently employed as a Software Engineer at Anyscale, a position he has held since 2023. He works in a hybrid capacity from San Francisco, California. In this role, he applies his extensive knowledge in software engineering and machine learning to contribute to the company's projects and initiatives.
Previous Experience at Amazon
Prior to his current role, Gene worked at Amazon as a Machine Learning Engineer from 2020 to 2023. During his three years in this position, he operated in a hybrid work environment in the San Francisco Bay Area. His responsibilities included developing and implementing machine learning models to enhance Amazon's services.
Educational Background
Gene holds a Master's Degree in Computer Science from the University of California, Davis, which he completed from 2014 to 2016. He also earned a Bachelor's Degree in Applied Mathematics with a Physics emphasis from the University of California, Merced, from 2009 to 2013. Additionally, he completed a Nanodegree in Machine Learning from Udacity in 2016.
Technical Skills and Expertise
Gene possesses expertise in a variety of programming languages, including Ruby, Python, Java, C++, Objective-C, Matlab, and R. He is proficient in using technologies such as Kubernetes, Docker, Ruby on Rails, Kafka, Spark, AWS, and Microsoft Azure. His experience also includes working with various data stores like Elasticsearch, MySQL, MongoDB, PostgreSQL, SQLite, and Redis.
Research and Projects
Throughout his career, Gene has engaged in several research projects. Notably, he built back-end and data analysis for an automated animal tracking project as a graduate student researcher at UC Davis. He also completed a deep learning project focused on recognizing numbers in real-world images and worked on a machine learning project applying classification algorithms to real-world radio pulse data.