AI & HPC Performance Engineering Intern

About the Role

Hewlett Packard Enterprise is the global edge-to-cloud company advancing the way people live and work. We help companies connect, protect, analyze, and act on their data and applications wherever they live, from edge to cloud, so they can turn insights into outcomes at the speed required to thrive in today’s complex world.

Key Responsibilities

• Support senior team members on assigned technical projects.
• Help identify and troubleshoot technical issues, providing support and suggesting solutions.
• Assist with maintaining and updating hardware, software, and other technical systems.
• Participate in team activities, attend team meetings, learn project methodologies, and collaborate effectively with colleagues.
• Actively engage in learning about new technologies and methodologies relevant to work.
• Fulfill tasks and responsibilities assigned by a supervisor in a timely and efficient manner.
• Participate in periodic reviews to share updates and incorporate feedback on assigned projects/initiatives.

Requirements & Tech Stack

• Currently pursuing a Bachelor's degree in Engineering, Computer Science, Information Technology, or a related technical field.
• Familiarity with programming languages or technical tools relevant to the role.
• Linux system administration and command-line environments across multiple enterprise Linux distributions.
• AI model training, inference, benchmarking, performance characterization, and optimization.
• Basic programming or scripting knowledge (e.g., Python, Java, C++).

Qualifications

• Strong analytical, troubleshooting, and root-cause analysis skills.
• Strong interest in high-tech and a passion for learning.
• Excellent communication and interpersonal skills.
• Strong problem-solving and analytical skills.
• Time management skills and working with strict deadlines.
• A collaborative, solution-focused mindset and overall sense of urgency.

Nice-to-have Skills

• Basic knowledge on GPU accelerator technologies, memory hierarchies, and AI software stacks including CUDA, NCCL, and related ecosystem tools.
• Basic knowledge on high-performance computing (HPC), MPI, and Linux clusters.