AI/ML Developer Intern

Hewlett Packard Enterprise•San Jose, CA

3d•$35 - $41•Hybrid

About The Position

The AI/ML Developer Intern develops end-to-end technical solutions for mission-critical applications, using data and innovative methods to prototype, design, and implement cloud-based solutions. The role collaborates with project managers, development partners, and domain experts to build, deploy, operate, monitor, and support AI/ML models. It also contributes to innovative AI/ML service offerings across systems, platforms, and applications, including solutions for wireless and wired network diagnostics, root-cause analysis, remediation, and optimization.

Requirements

Working toward a bachelor's, master's, or PhD degree in computer science, data science, mathematics, physics, statistics, or a related field; master's degree candidates are strongly preferred.
Familiarity with and understanding of software systems design.
Proficiency in Python, including PySpark and Scikit-learn, and experience with software engineering best practices.
Basic understanding of object-oriented programming.
Strong background in statistical and machine-learning techniques, including anomaly detection, clustering and event ranking, time-series analysis, event-stream mining, hypothesis testing, causal inference, and deep learning.
Ability to communicate concepts and results effectively, with strong data-visualization skills.
Relevant industry experience in data science and machine learning.

Nice To Haves

Experience with online learning algorithms, reinforcement learning, semi-supervised learning, or mixed time-series/event streams.
Familiarity with generative AI and large language models (LLMs).
Experience with Agentic AI.

Responsibilities

Collaborate with cross-functional teams to design, develop, and implement AI/ML solutions tailored to business needs.
Work with domain experts to identify and formalize machine-learning problems involving wireless and wired network diagnostics, root causing, problem remediation, and optimization.
Design, implement, and validate machine-learning algorithms on big data.
Guide and oversee deployment of machine-learning solutions and monitor their operation.
Use Agentic AI to solve networking problems.
Identify and debug code and integration issues, and develop solutions that fit application architecture.
Develop and execute comprehensive feature test plans addressing performance, scale, usability, and security requirements.
Deploy AI/ML systems and application code using continuous integration and deployment pipelines.
Contribute to innovation and integration of new technologies into projects.
Analyze science, engineering, business, and other data-processing problems and develop solutions to complex application, system administration, or networking problems.