Honeywell · 4 days ago

Artificial Intelligence/Machine Learning - Summer 2025 Intern (US Person Required)

United States

Internship

Remote

Intern

$25/hr - $38.50/hr

Start in 2025 Summer

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Growth Opportunities

No H1B

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Responsibilities

Model, build and test AI software to ensure it can take large swatch of data and achieve desired results

Use Python, JavaScript and C++ to create a faster, more capable AI

Evaluate statistics, data and algorithms to increase productivity of assets, people and processes in industrial applications/use cases

Work with team of machine-learning and data engineers to develop seamless AI and integration

Stay current on AI knowledge, trends, and regulations

Collect, organize, and present progress with team leadership and stakeholders

Qualification

Find out how your skills align with this job's requirements. If anything seems off, you can easily click on the tags to select or unselect skills to reflect your actual expertise.

PythonJavaScriptC++Data ScienceAlgorithmsStatisticsAI/ML SolutionsData Warehousing

Required

Must be currently pursuing a bachelor’s degree (or higher) from an accredited college or university in Engineering, Computer Science or similar field

Must graduate December 2025 or later

Must continue enrollment in degree program upon completion of the internship

Validated academic excellence (3.0 GPA and higher)

Due to compliance with U.S. export control laws and regulations, candidate must be a U.S. citizen, U.S. permanent resident, or have asylum or refugee status in the U.S.

Understanding of data science, linear algebra, algorithms, and statistics

Demonstrated ability to develop efficient and high-quality software and in-depth knowledge of computer programming language such as Python, JavaScript, and C++

Experience in developing and deploying end-to-end AI/ML solutions

Excellent problem-solving skills with the ability to thrive in a demanding fast-paced work environment

Strong interpersonal and communication skills and a willingness to collaborate cross-functionally with different teams