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From WikiAlpha
  1. Ryan Scherbarth
    • Profession:** Software Engineer — Machine Learning & 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/in/ryanscherbarth](https://www.linkedin.com/in/ryanscherbarth)

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    1. Overview

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.

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.

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    1. Education

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.

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.

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    1. Early Career and Professional Experience
      1. Tesla — Machine Learning & HPC Infrastructure

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]

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.

      1. University of New Mexico — Center for Advanced Research Computing (CARC)

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:

- Assisting with system administration and user support across multiple HPC clusters - Managing and resolving user help tickets for computational researchers - Benchmarking and profiling compute systems for performance improvements - Conducting workshops on HPC fundamentals and user onboarding - Contributing documentation and automation for system monitoring and maintenance

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.

      1. Air Force Research Laboratory — Software Engineer Intern

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.

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    1. High-Performance Computing Competitions

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.

      1. SC Student Cluster Competition

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 "mystery applications." Teams must manage hardware configuration, power efficiency (limited to 3000 watts), and software optimization — all under time constraints and public scrutiny.

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]

Under his leadership: - 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. - He oversaw **system architecture design**, **hardware procurement**, and **benchmarking strategy**, directly leading the technical direction of the team. - 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] - His leadership extended to mentorship and teaching, training incoming team members on cluster design, system provisioning, and parallel performance tuning.

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.

      1. Winter Classic Invitational & NASA Competitions

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]

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    1. Technical Competencies

Ryan’s background blends **software engineering**, **systems optimization**, and **applied machine learning infrastructure**. His technical skill set includes:

- **Parallel and Distributed Computing:** MPI, OpenMP, CUDA, NCCL - **Performance Benchmarking & Profiling:** HPL, HPCG, NAMD, MLPerf - **Cluster Administration:** SLURM, Lustre, Linux systems management - **Machine Learning Infrastructure:** Distributed training environments, containerized pipelines (Docker/Singularity), GPU scheduling - **Programming Languages:** Python, C++, Bash, CUDA, Go - **Version Control & CI/CD:** Git, Jenkins, containerized build environments - **System Monitoring & Efficiency Tools:** nvidia-smi, perf, htop, tegrastats, Prometheus

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.

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    1. Professional Recognition

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]

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.

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    1. Career Outlook

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.

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**.

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    1. References

[^1]: [ryanscherbarth.com — Personal Website](https://www.ryanscherbarth.com) [^2]: [LinkedIn — Ryan Scherbarth](https://www.linkedin.com/in/ryanscherbarth) [^3]: [UNM Center for Advanced Research Computing — CARC Staff Pages](https://carc.unm.edu) [^4]: [UNM News — CARC Team Gears Up for SC24 Competition](https://news.unm.edu/news/carc-team-gears-up-for-sc24-competition) [^5]: [Resume — ryanscherbarth.com/resume](https://www.ryanscherbarth.com/resume) [^6]: UNM CS491 / CS591 HPC Course Materials [^7]: [SC24 Student Cluster Competition — Supercomputing.org](https://sc24.supercomputing.org/2024/09/teams-compete-in-the-ultimate-hpc-challenge-at-sc24/) [^8]: [UNM Roadrunner HPC Team News — carc.unm.edu](https://carc.unm.edu/news--events/News/hpc-competitions-2023.html) [^9]: [GPU Benchmarking Notes — ryanscherbarth.com/projects](https://www.ryanscherbarth.com/projects) [^10]: [H100 Power Efficiency Analysis — ryanscherbarth.com/blog](https://www.ryanscherbarth.com/blog) [^11]: [UNM/NASA HPC Collaboration News](https://news.unm.edu/news/partnering-for-success-computer-science-students-represent-unm-in-nasa-and-supercomputing-competitions)

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    1. External Links

- Personal Website: [1](https://www.ryanscherbarth.com) - LinkedIn: [2](https://www.linkedin.com/in/ryanscherbarth) - UNM CARC HPC Center: [3](https://carc.unm.edu) - SC24 Competition Coverage: [4](https://sc24.supercomputing.org)

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