Research Computing Engineer; AI Infrastructure and HPC)

Position Specifics

The Institute for Computational and Data Sciences (ICDS) at Penn State seeks a Research Computing Engineer to join its technical team. This role supports Penn State’s research mission by designing, operating, automating, and optimizing the GPU, AI, and computing infrastructure used by researchers across the university, along with the high-performance computing systems that support it. The position will be filled at the Research Computing Systems Engineer - Advanced Professional level.

Candidates must be U.S. citizens due to specific access requirements associated with this position.

This position is ideal for an engineer who enjoys building reliable, scalable systems for machine learning, data-intensive research, and advanced computing workloads. The successful candidate will work across GPU systems, HPC platforms, storage, networking, automation, and user-facing research workflows to enable cutting-edge research in AI, simulation, and computational science.

Work Arrangement:
This is a full-time position, which will report to the Research HPC Manager and requires on-site work at University Park and is not supportive of remote work.

• Collaborate with teammates, users, and vendor support to diagnose issues and implement solutions across compute, storage, networking, and software environments
• Monitor, maintain, automate, and improve AI and HPC systems and supporting infrastructure
• Design, deploy, operate, troubleshoot, and optimize systems using Dev Ops and infrastructure-as-code practices
• Support GPU-accelerated computing environments for AI, machine learning, and scientific workloads
• Partner with researchers and ICDS staff to understand workload requirements and develop engineering solutions for system configuration, performance, and research workflows
• Support security, logging, documentation, and compliance processes for the systems operated, including environments subject to federal research security
• Contribute to planning, requirements gathering, process improvement, and operational readiness for new services and infrastructure
• Provide timely updates to system documentation and respond to user questions with clear, actionable guidance
• Evaluate and improve tools, platforms, and workflows that support AI model development, training, inference, and data movement at scale

• Administration of multi-GPU nodes at scale, including driver and firmware lifecycle management, NVLink/NVSwitch topology validation, and GPU health monitoring and tuning for multi-GPU or multi-node GPU workloads
• Ability to work effectively in a Linux environment, including command-line tools, file editing, POSIX permissions, and system configuration
• Strong scripting ability in Bash and Python
• Strong problem-solving skills and the ability to debug complex systems
• Ability to work collaboratively as part of a technical team
• Experience using AI tools or AI agents to improve programming, debugging, development, or prototyping workflows
• Clear written and verbal communication skills

Experience with any of the following is helpful but not required:

• Experience supporting AI/ML infrastructure for model training, inference, experiment workflows, and large-scale data processing
• Experience with job schedulers such as Slurm, PBS, HTCondor, or LSF
• Software development experience with HPC programming environments such as C/C++, Fortran, CUDA, MPI, or OpenMP
• Dev Ops experience, including Git-based workflows, CI/CD, automation, and collaborative development practices
• Experience with deployment tools such as xCAT, Warewulf, OpenCHAMI, Open Stack/Bifrost, or MAAS
• Experience administering or supporting Kubernetes
• Experience with virtualization/containerization technologies such as VMware, Docker, Apptainer, or Podman
• Networking experience including EVPN, BGP, and IPv6
• Experience with high-speed interconnects such as Infini Band or HPE Slingshot
• Experience with HPC or distributed storage systems such as GPFS, Lustre, Ceph, or VAST
• Monitoring and observability experience with tools such as Grafana, Graphite, Prometheus, Victoria Metrics, or InfluxDB
• Experience with databases such as MySQL/MariaDB or PostgreSQL
• Security experience including identity and access management, single sign-on, and LDAP/Active Directory
• Familiarity with Agile project development
• Prior experience in academic research computing, research data infrastructure, or large-scale shared computing environments