Wireless Networking Intern

About the Role

• This role is designed as 'Onsite' with the expectation that you will primarily work from an HPE office.
• The HPE Networking CTO Wireless Lab is seeking intern candidates for up to three months.
• The internship involves learning new testing tools, methodologies, and analyzing/enhancing performance in a real-world setting.
• Interns will gain experience in how HPE wireless networking products work from a feature/functionality perspective.
• The role requires application of neural networking and machine learning techniques to train data analysis models.
• Interns will participate in gathering and analyzing test data.
• They will help configure and deploy lab equipment and apply networking concepts to a test environment.
• The intern will develop a report to be delivered to HPE Networking management.

Key Responsibilities

• Analyze and enhance performance in a real-world setting using wireless performance testing.
• Apply neural networking and machine learning techniques to train data analysis models.
• Gather and analyze test data.
• Configure and deploy lab equipment.
• Apply networking concepts to a test environment.
• Develop a report for HPE Networking management.

Requirements & Tech Stack

• Python, bash, and/or other relevant scripting languages.
• Data analysis experience using tools such as SQL or Python pandas.
• ML/DL concepts and hands-on experience building, training, and deploying models using frameworks like TensorFlow, PyTorch, Scikit-Learn.
• Ability to leverage AI tools such as Github Copilot, Microsoft Copilot, or Claude code.
• Proficiency with MS Office Excel, Word, PowerPoint or equivalent software.
• Familiarity with iOS, MacOS, Android, Windows, Linux operating systems.
• Experience with simple "end-to-end" network performance tests (e.g., Speedtest.net, iPerf, ping).
• General radio/RF training or experience.
• Basic training in statistics.

Qualifications

• Currently pursuing a PhD program in a relevant field such as Computer Science, Computer Engineering, Electrical Engineering, Data Science, or a related quantitative discipline.
• University students enrolled in a PhD program engaged in a technical or non-technical internship role.