Joseph Meadows

Building automation, utilities, and supporting infrastructure around the simulation environment. I have developed tools that reduced several-month development cycles to less than one month under ideal circumstances.

Writing clear, testable requirements for hardware and simulation components, linking them to system-level needs, and maintaining traceability through the Verification & Validation process. Ensures every model behavior is justified, measurable, and aligned with mission objectives.

Building physics-based or behavior-based models of various vehicle systems and environmental components that act on the vehicle. This includes replicating interfaces, capturing vehicle dynamics, and ensuring the models accurately reflect real-world performance envelopes.

Systematically confirming that models behave as intended (verification) and accurately represent the real system (validation). Involves test case design, targeted unit testing, comparison against analytical or empirical data, and documenting compliance.

Evaluating how the modeled system performs under nominal, stressed, and edge-case conditions involves analyzing technical performance metrics, and diagnosing the root causes of performance degradation before it impacts mission objectives.

Designing how individual component models connect into a larger simulation environment. Includes interface definition, data flow management, timing / synchronization, and ensuring subsystem interactions behave realistically.

All the previously mentioned skills culminate into thoroughly-written technical reports that explain how the system works, how it is tested, and its current stage in the Verification & Validation process.

Developed a system that allows complex, multi-run simulation data and flight test data to be processed using the exact same steps. This eliminated special case logic and dramatically simplified analysis across heterogeneous data sources.

Implemented optimizations that significantly reduced execution time and developer iteration time. Improved throughput for large datasets and accelerated analysis cycles across the team.

Unified plotting styles, color systems, annotation rules, and customer-ready visual outputs regardless of data source or engineer.

Built pipelines that inject processed simulation results directly into report and presentation templates, enabling rapid turnaround for customer-requested changes and reducing the mistakes that occur due to human error.

Created visualizations tailored for aerospace system-level validation, including advanced time-aligned plotting techniques and novel simulation grading techniques

Experience building small-scale physics simulations (e.g. gravity-driven motion) using numerical integration techniques like RK-4 integration. Demonstrated ability to translate physical laws into computational models.

Developed a satellite orbit simulation from sparse ground-based observations. Experience with state estimation, orbital propagation, and interpreting physical constraints from limited data.

After determining that GRAM was not sufficient for environmental modeling in flight test reconstruction, I theorized multiple atmospheric models that could fill the gap, and was given the opportunity to fully develop one. It remains the most valuable model in system-level validation.

Worked on modeling propulsion system behavior and performed academic projects involving nozzle flow using the Method of Characteristics. Shows exposure to compressible flow, pressure/temperature relationships, and gas dynamics.

Simulation inputs and outputs are processed using a comprehensive data pipeline in Python. We consistently process tens of millions of data points per flight.

The logical models of the core flight software and flight simulation are written in C++ for highly-optimized performance. They use a modular design to facilitate extensibility, maintainability, and efficient compilation times.

Rust powers the core simulation engine at Catosphere LLC. It is the language of choice for portability, memory safety, performance, easy unit testing, and thorough documentation.

MATLAB is used for post-processing and analysis of flight test data and simulation outputs. As an undergraduate it was used for simple data processing and numerical integration for an orbital mechanics simulation.

C is a unique language in that it allows you thorough management of raw memory and hardware boundaries. It was taught at the University of Arizona, and has been useful to my work at RTX.

I have successfully deployed Cython to speed up certain data processing tasks at RTX by over 100 times. We don't use it often, but targeted use cases exist that can make a large impact.

I have used Svelte extensively in my work at Catosphere LLC and prior personal projects. It powers highly interactive front-end development with minimal boilerplate.

Type-safe reactive state management and front-end architectures.

Legacy aerospace computation modules and numerical modeling.

Bash / Nu and Batch are used for various automation and deployment tasks both at RTX and Catosphere LLC.

I have used Java for various hobbyist and class projects in the past. I am familiar with the language but wouldn't choose to use it if I were starting a new project. Kotlin is a huge improvement, but I also don't use it often.

Many of my past web projects have been powered by PHP in one way or another. Since switching to backends written in Rust and frontends written in Svelte, I have not picked PHP back up.

Lua was my introduction to programming as a 9 year old playing Roblox. I haven't used it often since then, but I have worked on similar projects with embeddable scripting languages.

Building hyper-fast local-first user interfaces using modern runes.

Architecting asynchronous, lightweight, and scalable web backends.

Utility-first layout construction focused on clean, fluid responsive design.

Through Rust, I am using WASM to enable lightweight, fast, and secure web applications that primarily run on the user's device.

Optimizing highly scalable web applications running on minimal hardware through Cloudflare's generous cache and CDN.

Managing complex homelab environments, storage arrays, and GPU pass-through for powering high-performance computing with safety.

Relational data structuring, heavy analytical indexing, and local syncing.