AI Research Engineer
I'm an AI Research Engineer working on experimental neural architectures, including recurrent and state-space models, memory, and generative world models. I implement ideas from research papers, train and benchmark prototypes, investigate failures, and iterate based on controlled experiments.
I'm a Computer Engineering graduate from Penn State University. A lot of my model research has been done on consumer hardware, where architecture and efficiency choices matter. Outside of AI research, I also build and maintain open-source C++ software used by hundreds of thousands of people.
Independent model research in sequence modeling, memory, reasoning, and generative representations. Work includes architecture prototyping, controlled comparisons, reproducible evaluation, and analysis of generalization failures.
I built an action-conditioned world model around a sparse MoE DiT and trained it on my own data using a single RTX 4090. I experimented with the architecture and conditioning, then built targeted diagnostics and benchmarks to find failure points and inform the next iteration.
I built an open-source C++ replay system that can record and visualize thousands of Geometry Dash attempts at once in real time. The replay data is quantized and keyframed to reduce size. Most data stays on the disk until it is needed. Time-windowed indexing keeps the system from checking attempts that cannot matter yet. Player objects are reused and ghosts can skip work when they are not relevant to the current section. I update and maintain this project across Windows, Android, macOS, and iOS, with over 530k unique installs.
At Penn State University, I train and evaluate computer vision models for an insect-monitoring research project, build tools to make the field pipeline easier to monitor and debug, and develop the downstream workflow for organizing research data. A related research paper is in preparation, so I am keeping unpublished technical details off this site for now.
Hands-on work in model research and applied ML.
I work on the ML and software side of an insect-monitoring research project. My work includes model training and experiments, maintaining and adding features to the pipeline, and organizing and creating software for processing research data.
I work on experimental model architectures in a small independent research group. I implement and benchmark model changes, then build targeted tests when training behavior is unclear.
I worked on AnimateDiff training code for custom video-model experiments. I modified and debugged the training pipeline and helped with multi-GPU compatibility and Linux training issues. I demonstrated progress to the client during weekly meetings.
I'm looking for Research Engineer roles where I can spend most of my time implementing model ideas, running experiments, and iterating on architectures. I work best with room to own technical problems while still having strong researchers and engineers around to challenge assumptions and compare results.