Infrastructure & Ride and Fleet Software Teams

• Streamlined demo and testing operations by creating admin interfaces, API access to the cloud, a fleet reservation system, and automated ride-hail issue triage.

• Sped up container builds by 2-10x and eliminated the vast majority of unexpected build failures.

• Rewrote the on-vehicle fleet dispatch and ride state module from Python to C++, while also modifying it to run asynchronously and use established internal communication frameworks.

Ride State Interface Rework

• Architected a new cloud-to-vehicle ride state information pipeline in an initiative-led project to modernize years-old legacy demo requirements and address longstanding development limitations.

• Built a multi-component cross-team ride state integration test framework, reducing the reliance on physical vehicle and operator testing and drastically hastening development iteration time.

• Enabled ride and fleet teams to define their own modular component requirements as well as run integrated regression tests.

Cloud Infrastructure Team

• Proposed and executed a full restructure of the cloud environment’s bootstrapping process, improving success rates and simplifying procedures for deploying features and fixes.

• Reduced the downtime from large upgrades by a factor of three by overhauling and parallelizing the processes across various internal services.

• Configured intrusion detection for customer data and internal files on Windows servers, assisting with SOC2 audit compliance.

Repository Import Server

• Automated the migration of terabytes of on-premises customer document repositories into the cloud.

• Converted existing SQL data sanitization queries from iterative updates to broad updates, reducing their collective runtime from hours to minutes.

• Enhanced features and performance based on feedback from customer support.

• Integrated Conduit, an open-source data exchange library, to communicate between physics simulation codes and other programs using a common data format.

• Utilized Conduit objects as timing trees to analyze and record function performance across MPI threads.

• Coordinated as a team of four and reported to a liaison and a professor on a weekly basis.

• Tutored students and graded assignments for introductory, data structure, and computability classes.