Electrical/Hardware Engineering Intern

HP•Corvallis, OR

2d•$45 - $52•Onsite

About The Position

This internship will focus on building connected, end-to-end datasets and workflows that enable more efficient R&D operations and decision-making. The intern will improve data and process flows across experimentation, data integration, data validation, and AI/ML-driven insight generation. This opportunity is intended for conversion to a full-time role that will not offer work authorization sponsorship in the future (full-time conversion pending performance evaluation post internship and available headcount). Interested candidates must be currently eligible to work in the US AND must not require work authorization sponsorship in the future. HP, Inc. will not provide any assistance or sign documentation in support of immigration sponsorship including Curricular Practical Training (CPT) or Optional Practical Training (OPT).

Requirements

Currently pursuing a Ph.D. in Computer Science, Mechanical Engineering, Chemistry, Chemical Engineering, Data Science, or a related technical field.
Must be enrolled full time at an accredited university.
Has no more than 24 months of prior work experience professionally related to the intern requisition they are applying to (not including internships, teaching assistance, research work, or military experience).
Able to obtain work authorization in the United States in 2027, and not require sponsorship in the future.
Experience with programming, data analysis, databases, or engineering data workflows.
Understanding of data integration, validation, and software-development principles.
Strong analytical, project-management, and technical communication skills.

Nice To Haves

Python and SQL.
SQL Server and data-pipeline concepts.
Data analysis and visualization.
Artificial intelligence and machine learning.
Pipeline Pilot, GitHub Copilot, or similar development and AI tools.

Responsibilities

Design and build connected datasets and repeatable data pipelines for R&D applications.
Integrate data from multiple sources and improve data-joining processes.
Develop validation methods that improve data quality, consistency, and reliability.
Create Python and SQL solutions that streamline engineering workflows and reduce manual tasks.
Apply AI and machine-learning methods to experimental data to identify patterns and generate insights.
Document technical solutions, results, and recommended process improvements.
Collaborate with engineering, software, and data stakeholders to deliver project objectives.