Modernizing the F-35 Integrated Test Force
Year
2025-2026
I worked as an Aeronautical Engineer at Lockheed Martin with the F-35 Program. To ensure smooth data collection, software configuration changes, structural maintenance, and troubleshooting, I adjusted an extensive dataset of protocols specific to the aircraft that influence how customers and the military fleet function in real-time.
Leveraging my knowledge of machine learning and artificial intelligence, I integrated Lockheed Martin proprietary technology to increase our test force efficiency. I developed a showcase of secure, agentic RAG for a breadth of confidential standard operating procedures (SOPs), contact lists, approved guidelines, software information, and basic aircraft documentation for project managers. This LLM agent (based on Llama Scout) has since, greatly increased the efficacy of seasoned and new recruits by providing references and direction for a given problem.
With awareness of advanced DoD-partnered innovation, I brought in new technology for the F-35 program to make use of our unique testing environment. Leadership behind automation programs, pilot training, and AI are some of the branches I was coordinating with to help Lockheed Martin labs assess performance, improvement points, and acquire real-use data.
Disciplines
Artificial Intelligence
Test Design
Manufacturing
Procurement
Data Analysis
Deploying Guidance, Navigation, and Controls for Reentry Vehicles
Year
2026
After working with the F-35 Integrated Test Force, I transitioned into Guidance, Navigation, and Controls working on reentry vehicle autonomy with Lockheed Martin Space.
My work centers on developing C++/Python flight control architecture, high-fidelity C++/MATLAB/Simulink 6-DOF simulations, and validating closed-loop GNC behavior across SITL and HITL environments.
A meaningful portion my work is post-flight forensics. When classified test data shows behavior that diverges from prediction, I work to reconstruct the aerodynamic environment and correlate sensor telemetry against analytical models to isolate where assumptions diverge. That diagnostic work feeds back into the codebase as algorithm updates that meet evolving customer performance requirements.
The day-to-day spans C++, Python, MATLAB, and Linux, with HPC accelerated simulations and the team operating on an Agile DevOps workflow. I collaborate closely with performance analysts and the flight software team, working at the intersection of control theory, algorithm development, and systems engineering.
Through research and development, I continue to bridge the sim-to-real gap.
Disciplines
Control Theory
Physics
Simulation
HPC
HITL/SITL
Flight Software
