Software Development Engineer at Amazon Robotics, developing scalable services and path planning algorithms that enable robots to navigate efficiently in dynamic environments.

I worked on a team of four interns to develop a web application to automate requesting, approving, creating, and cleaning SAN storage. Since the team consisted of only interns, I gained experience making code architecture decisions and deciding which customer deliverables needed prioritization. I led the efforts to build a Python API to interface with the switches and storage containers used for testing. In addition, I developed a large portion of the back-end Flask Rest API and the MongoDB database used by the application. The application we created was estimated to increase testing efficiency by 24%, leading to an anticipated savings of $1.45 million in savings per year.

Throughout my internship, I worked on both the testing and query management team (cmdcomp). On the testing team, I added a code static analysis tool to run nightly on the codebase through the Gitlab CI/CD pipeline and provide a report in the morning of bugs/errors found. I also built a dashboard to generate graphs of the information created by the CI/CD pipelines focusing on average queue time, run time, and the number of pipelines in a given time. These projects will help ensure that bugs are caught early in the development process and help the testing team find holes in the testing infrastructure. On the cmdcomp team, I worked on implementing the queries: commit(), dateadd(), datediff(), months_between(), and version(). These functions come from multiple different query languages such as SQL Server, PostgreSQL, and Oracle/PLSQL and will allow Ocient's database to be compatible with many different querying languages.

I worked side-by-side with full time employees in order to develop automated driving and mapping software. This involved using the ROS2 framework to send custom messages between different modules in the project. In addition, I built an algorithm to calculate lane level localization and lane center offset of vehicles based on vehicle coordinates and lane polynomials in Python. Finally, I built a data pipeline to interpret lane level map information and provide it in the right format for driving policy training and implementation.