Data Analysis Intern, Applied System Engineering

We are looking for a data analytics intern to work on datacenter telemetry information analysis and reporting. You will be part of the team developing the most exciting computing hardware and software and contributing to the latest breakthroughs in artificial intelligence and GPU computing.

Your main responsibilities will be:

• Working with a team passionate about large scale datacenter development and deployment. Assisting in analyzing large scale workloads and searching for application and infrastructure improvement opportunities.
• Developing and implementing a Machine Learning algorithm for rapid classification and prediction. The implementation will integrate into existing software toolsets for usability.
• Delivering actionable cluster insights by analyzing telemetry data and applying machine learning to assist engineers in building creative solutions based on NVIDIA technology.
• Researching and analyzing data, identifying trends of interest, linking changes to recorded events, crafting conclusions and helping the team make intelligent data-based decisions. Exploring methods to describe data and predict events using numerical methods and machine learning.
• Working closely with a mentor to learn more about analysis needs and communicating findings with team members.
• Documenting work and related materials to help us build on top of your contributions.

To succeed in this role, you should have:

• A strong background in Mathematics, Computer Science, Engineering, or another relevant field.
• Strong programming skills, especially in Python.
• Deep Learning framework skills.
• Understanding of core machine learning techniques and concepts.
• Shown debugging and problem-solving skills.
• Knowledge of Linux based operating systems, Python, Bash, C/C++.

It would be great if you had experience with Linux and bash scripting, exposure to high performance or large-scale computing environments and visualization techniques, and strong analytical skills with good attention to detail.