Research Computing Engineer (AI Infrastructure and HPC)

APPLICATION INSTRUCTIONS: CURRENT PENN STATE EMPLOYEE (faculty, staff, technical service, or student), please login to Workday to complete the internal application process . Please do not apply here, apply internally through Workday. CURRENT PENN STATE STUDENT (not employed previously at the university) and seeking employment with Penn State, please login to Workday to complete the student application process.

Please do not apply here, apply internally through Workday. If you are NOT a current employee or student, please click “Apply” and complete the application process for external applicants . Approval of remote and hybrid work is not guaranteed regardless of work location. For additional information on remote work at Penn State, see  Notice to Out of State Applicants . ​     POSITION SPECIFICS ​ The Institute for Computational and Data Sciences (ICDS) at Penn State seeks a Resea rch Computing Engineer to join our technical team.

This role supports Penn State’s resea rch mission by designing, operating , automating, and optimizing the GPU and AI and computing infrastructure used by resea rchers across the university , along with the high-performance computing systems that support it. The position will be filled at the Reserach 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 .

Responsibilities: As part of a collaborative engineering team, you’ll contribute across a broad range of responsibilities that include the following:   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.

D esign, deploy, operate , troubleshoot, and optimize systems using DevOps 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 practical engineering solutions for system configuration, performance, and research workflows. Support security, logging, documentation, and compliance process for the systems we operate , 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. Required qualifications and skills include the following:   Administration of multi-GPU nodes at scale, including driver and firmware lifecycle management, NVLink / NVSwitch topology validation, 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, developm