Resume#
I am a software engineer focused on machine learning systems, performance-sensitive implementation, and technical writing. My work sits at the point where models, tooling, and engineering constraints meet.
Profile#
I am most interested in work that requires both technical depth and engineering discipline: understanding model behavior, debugging failure modes, improving runtime performance, and explaining complex systems clearly. I tend to work from first principles, prioritize reproducibility, and prefer implementation that is measurable rather than performative.
Areas of Focus#
- Machine learning systems: model training, evaluation, debugging, and reproducible experimentation
- Performance-oriented engineering: practical optimization, profiling, and reasoning about bottlenecks
- CUDA and low-level ML infrastructure: learning the systems side of modern AI workloads
- Technical communication: writing tutorials and implementation notes that stay concrete
Technical Skills#
- Languages: Python, C++, CUDA, SQL, Bash
- ML and Data: PyTorch, NumPy, scikit-learn, experiment design, model evaluation
- Systems and Tooling: Git, Linux, profiling, debugging, reproducible workflows
- Development Practices: testing, documentation, benchmarking, code review, performance analysis
Current Work#
I am currently building a body of work through technical writing, hands-on ML implementation, and systems-focused experiments. This site serves as a public record of that work: tutorials, engineering notes, and project writeups that show how I think through problems and how I implement solutions.
Representative strengths include:
- turning broad ideas into runnable experiments and clear implementation steps
- debugging training instability, data problems, and evaluation mistakes
- writing technical explanations that connect intuition to code
- working across high-level ML workflows and lower-level performance concerns
Selected Strengths#
- Clear written communication for technical audiences
- Strong bias toward measurement, reproducibility, and debugging
- Comfort working across modeling, tooling, and systems details
- Consistent focus on practical engineering rather than surface-level demos
Education#
My strongest training so far has been project-driven: building, measuring, and documenting systems until the theory connects to the implementation. I prefer to present only concrete, verifiable credentials here rather than filler.
For a detailed resume or to discuss opportunities, please contact me.
