HPC Research Associate Job at Mondo, Waterville, ME

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  • Mondo
  • Waterville, ME

Job Description

About the Role

We are seeking a High Performance Computing (HPC) Research Associate to support computational research initiatives within a collaborative academic environment. In this role, you’ll work closely with faculty—particularly in computational chemistry and computational biotech —to ensure their HPC software and workflows are optimized, reliable, and accessible for day-to-day use.

This is a hands-on, user-facing position requiring strong technical problem-solving skills and an ability to communicate effectively with researchers. You will debug and improve HPC software performance, help faculty troubleshoot scheduling issues, and—if experienced—support hardware-level operations.

Key Responsibilities

  • Collaborate closely with faculty in computational fields to understand and support their HPC needs
  • Analyze and debug software issues to ensure optimal performance in a high-performance computing environment
  • Work with job scheduling tools such as SLURM to manage and troubleshoot workload distribution
  • Provide guidance and documentation for faculty to use HPC resources effectively in their research
  • Contribute to system-level optimization and, if experienced, assist with hardware-level troubleshooting and configuration
  • Ensure faculty HPC software is running smoothly and integrated into their research pipelines

Must-Have Qualifications

  • 3–5 years of hands-on experience in high-performance computing (HPC) environments
  • Strong working knowledge of SLURM job scheduling software
  • Proficiency in at least two of the following programming/scripting languages: Python, C, C++, Bash, R, MATLAB
  • Ability to effectively communicate and collaborate with academic faculty and research staff
  • Problem-solving mindset with the ability to analyze and resolve complex performance and compatibility issues

Nice-to-Have Qualifications

  • Experience supporting HPC hardware environments (e.g., clusters, nodes, interconnects)
  • Familiarity with performance profiling and tuning of computational research software
  • Previous experience in a research university or academic computing support setting

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