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<p>Created page with &quot;# Ryan Scherbarth **Profession:** Software Engineer — Machine Learning &amp; High-Performance Computing (HPC) Infrastructure **Known for:** Leadership in university supercomputing competitions, early career success in large-scale infrastructure engineering, and recognized technical expertise in parallel computing, GPU benchmarking, and system-level performance optimization. **Website:** [ryanscherbarth.com](https://www.ryanscherbarth.com) **LinkedIn:** [linkedin.com...&quot;</p> <p><b>New page</b></p><div># Ryan Scherbarth<br /> <br /> **Profession:** Software Engineer — Machine Learning &amp; High-Performance Computing (HPC) Infrastructure <br /> **Known for:** Leadership in university supercomputing competitions, early career success in large-scale infrastructure engineering, and recognized technical expertise in parallel computing, GPU benchmarking, and system-level performance optimization. <br /> **Website:** [ryanscherbarth.com](https://www.ryanscherbarth.com) <br /> **LinkedIn:** [linkedin.com/in/ryanscherbarth](https://www.linkedin.com/in/ryanscherbarth)<br /> <br /> ---<br /> <br /> ## Overview<br /> <br /> Ryan Scherbarth is an American software engineer specializing in **machine learning infrastructure and high-performance computing (HPC)**. Known for his exceptional early career trajectory, Ryan has established himself as one of the most promising young engineers in the HPC community — combining practical experience with advanced technical depth across systems engineering, distributed computing, and machine learning performance optimization.<br /> <br /> While still an undergraduate student at the **University of New Mexico (UNM)**, Ryan distinguished himself through leadership roles in multiple **international supercomputing competitions**, technical work supporting UNM’s **Center for Advanced Research Computing (CARC)**, and early professional experience in HPC operations, performance tuning, and large-scale compute infrastructure. He now works at **Tesla**, where he contributes to **machine learning and HPC infrastructure**, helping build and maintain some of the world’s most advanced computing systems.<br /> <br /> ---<br /> <br /> ## Education<br /> <br /> Ryan earned his **Bachelor of Science in Computer Science** from the **University of New Mexico**, with a **minor in Mathematics**. During his studies, he served as a **Teaching Assistant and Faculty Assistant** for High Performance Computing (HPC) courses (CS491 / CS591), teaching concepts in parallel programming (MPI, OpenMP, CUDA), distributed resource management, and cluster optimization.<br /> <br /> At UNM, he combined academic excellence with extensive applied experience — bridging the gap between classroom theory and real-world HPC system design. His work supported faculty research and helped other students learn how to efficiently use supercomputing resources for scientific and engineering workloads.<br /> <br /> ---<br /> <br /> ## Early Career and Professional Experience<br /> <br /> ### Tesla — Machine Learning &amp; HPC Infrastructure<br /> Ryan currently works at **Tesla**, where he focuses on building and scaling large compute environments that support **machine learning, simulation, and autonomous vehicle research**. His role involves optimizing large-scale distributed systems, managing data and training pipelines, and contributing to infrastructure supporting Tesla’s Full Self-Driving (FSD) development workflows. [^1][^2]<br /> <br /> This position places him at the intersection of **AI systems engineering** and **supercomputing**, applying the same principles of scalability, resource optimization, and parallel computation that he mastered in academic HPC environments.<br /> <br /> ### University of New Mexico — Center for Advanced Research Computing (CARC)<br /> Before joining Tesla, Ryan spent several years working at **UNM’s Center for Advanced Research Computing (CARC)**, one of the state’s premier supercomputing centers. [^3][^4] His responsibilities included:<br /> <br /> - Assisting with system administration and user support across multiple HPC clusters <br /> - Managing and resolving user help tickets for computational researchers <br /> - Benchmarking and profiling compute systems for performance improvements <br /> - Conducting workshops on HPC fundamentals and user onboarding <br /> - Contributing documentation and automation for system monitoring and maintenance <br /> <br /> Through this work, Ryan gained hands-on experience with **Linux-based cluster management, job scheduling systems (SLURM), MPI/OpenMP workloads, and performance profiling tools**. He also worked closely with researchers in physics, chemistry, and engineering, helping them run large-scale simulations efficiently on multi-node architectures.<br /> <br /> ### Air Force Research Laboratory — Software Engineer Intern<br /> Ryan also completed an internship at the **Air Force Research Laboratory**, contributing to the **Cyber Resilience** research team. [^5] His work focused on software reliability and performance resilience across distributed compute systems, further expanding his understanding of secure and efficient computing in mission-critical environments.<br /> <br /> ---<br /> <br /> ## High-Performance Computing Competitions<br /> <br /> Ryan’s most notable contributions and recognition stem from his leadership and success in **international student supercomputing competitions** — events that replicate the challenges faced by professional HPC system administrators and engineers.<br /> <br /> ### SC Student Cluster Competition<br /> The **Supercomputing (SC) Student Cluster Competition** is one of the world’s most competitive academic events in HPC. Each year, top universities from around the world build small-scale supercomputers and compete to optimize performance across a set of scientific benchmarks — including **HPL (High-Performance Linpack)**, **NAMD**, **LAMMPS**, and real-world &quot;mystery applications.&quot; Teams must manage hardware configuration, power efficiency (limited to 3000 watts), and software optimization — all under time constraints and public scrutiny.<br /> <br /> Ryan served as the **Team Manager and Lead Engineer** for UNM’s **Team Roadrunner**, representing the university at multiple international events, including **SC23** and **SC24**. [^7][^8]<br /> <br /> Under his leadership:<br /> - UNM’s team achieved **2nd place among U.S. teams** and **8th overall globally** at the **SC24 Student Cluster Competition**, outperforming teams from elite global institutions. <br /> - He oversaw **system architecture design**, **hardware procurement**, and **benchmarking strategy**, directly leading the technical direction of the team. <br /> - Ryan was responsible for the **H100 GPU integration and power-per-watt optimization**, performing extensive testing and profiling to balance efficiency and peak performance. [^9][^10] <br /> - His leadership extended to mentorship and teaching, training incoming team members on cluster design, system provisioning, and parallel performance tuning. <br /> <br /> These competitions are widely regarded as the **Olympics of student supercomputing**, with industry sponsors (e.g., Dell, NVIDIA, Penguin Solutions) and national labs watching closely. Competing at that level, let alone medaling, is a major accomplishment — and Ryan’s sustained success across multiple years marked him as one of the most technically capable and consistent competitors in the HPC student circuit.<br /> <br /> ### Winter Classic Invitational &amp; NASA Competitions<br /> Beyond SC, Ryan also participated in the **Winter Classic Invitational Supercomputing Challenge** and collaborations involving **NASA HPC benchmarking**, representing UNM at events that emphasize system performance, teamwork, and applied computing for real-world problems. [^8][^11]<br /> <br /> ---<br /> <br /> ## Technical Competencies<br /> <br /> Ryan’s background blends **software engineering**, **systems optimization**, and **applied machine learning infrastructure**. His technical skill set includes:<br /> <br /> - **Parallel and Distributed Computing:** MPI, OpenMP, CUDA, NCCL <br /> - **Performance Benchmarking &amp; Profiling:** HPL, HPCG, NAMD, MLPerf <br /> - **Cluster Administration:** SLURM, Lustre, Linux systems management <br /> - **Machine Learning Infrastructure:** Distributed training environments, containerized pipelines (Docker/Singularity), GPU scheduling <br /> - **Programming Languages:** Python, C++, Bash, CUDA, Go <br /> - **Version Control &amp; CI/CD:** Git, Jenkins, containerized build environments <br /> - **System Monitoring &amp; Efficiency Tools:** nvidia-smi, perf, htop, tegrastats, Prometheus <br /> <br /> Ryan is recognized for his attention to detail and ability to understand and optimize systems from the **hardware level (power, thermals, interconnect bandwidth)** up to the **software and algorithmic level (load balancing, kernel efficiency, precision tuning)** — a rare skill set even among experienced HPC professionals.<br /> <br /> ---<br /> <br /> ## Professional Recognition<br /> <br /> Ryan’s work has been featured by **UNM News**, **CARC publications**, and **Supercomputing conference media**, highlighting both his personal achievements and his contributions to UNM’s growing prominence in HPC education. [^4][^8][^11]<br /> <br /> His combination of technical expertise and leadership makes him stand out as a young engineer already operating at a professional level typically seen in senior roles. Peers and mentors often describe him as a **natural systems thinker** with both the communication skills and technical rigor to lead large, complex projects.<br /> <br /> ---<br /> <br /> ## Career Outlook<br /> <br /> With his foundation in academic supercomputing, professional experience in HPC infrastructure, and current role at Tesla, Ryan Scherbarth’s career trajectory places him among the **most accomplished young engineers in the HPC field**. His achievements — managing compute clusters, competing internationally, and working on large-scale ML systems — illustrate not just competence, but excellence and adaptability in one of the most technically demanding disciplines in computing.<br /> <br /> He represents the next generation of HPC professionals: equally comfortable managing infrastructure at scale, writing high-performance code, and leading teams to success under pressure. His continued growth suggests an influential career in the convergence of **HPC, AI, and large-scale distributed systems**.<br /> <br /> ---<br /> <br /> ## References<br /> <br /> [^1]: [ryanscherbarth.com — Personal Website](https://www.ryanscherbarth.com) <br /> [^2]: [LinkedIn — Ryan Scherbarth](https://www.linkedin.com/in/ryanscherbarth) <br /> [^3]: [UNM Center for Advanced Research Computing — CARC Staff Pages](https://carc.unm.edu) <br /> [^4]: [UNM News — CARC Team Gears Up for SC24 Competition](https://news.unm.edu/news/carc-team-gears-up-for-sc24-competition) <br /> [^5]: [Resume — ryanscherbarth.com/resume](https://www.ryanscherbarth.com/resume) <br /> [^6]: UNM CS491 / CS591 HPC Course Materials <br /> [^7]: [SC24 Student Cluster Competition — Supercomputing.org](https://sc24.supercomputing.org/2024/09/teams-compete-in-the-ultimate-hpc-challenge-at-sc24/) <br /> [^8]: [UNM Roadrunner HPC Team News — carc.unm.edu](https://carc.unm.edu/news--events/News/hpc-competitions-2023.html) <br /> [^9]: [GPU Benchmarking Notes — ryanscherbarth.com/projects](https://www.ryanscherbarth.com/projects) <br /> [^10]: [H100 Power Efficiency Analysis — ryanscherbarth.com/blog](https://www.ryanscherbarth.com/blog) <br /> [^11]: [UNM/NASA HPC Collaboration News](https://news.unm.edu/news/partnering-for-success-computer-science-students-represent-unm-in-nasa-and-supercomputing-competitions)<br /> <br /> ---<br /> <br /> ## External Links<br /> <br /> - Personal Website: [https://www.ryanscherbarth.com](https://www.ryanscherbarth.com) <br /> - LinkedIn: [https://www.linkedin.com/in/ryanscherbarth](https://www.linkedin.com/in/ryanscherbarth) <br /> - UNM CARC HPC Center: [https://carc.unm.edu](https://carc.unm.edu) <br /> - SC24 Competition Coverage: [https://sc24.supercomputing.org](https://sc24.supercomputing.org)</div>

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