AI Developer Co-op — Real-World ML & Data Systems

Manhattan Associates; United States; Remote; USD 34,000 - 48,000; Full time

Benefits offered by this job

Mentorship

Hands-on AI projects

Diversity & inclusion

Job summary

Manhattan Associates in Boston, MA, seeks a motivated AI Developer Co-op currently pursuing a degree in Computer Science, AI, or Data Science. You will apply classroom knowledge to real-world supply chain AI problems, building, testing, and improving AI-driven solutions alongside experienced developers.

You’ll work full-time, collaborate with engineers and business partners, and gain hands-on experience across experimentation, data processing, and documentation in a supportive, inclusive

Qualifications

• Currently enrolled in a bachelor’s or master’s degree in Computer Science, AI, Software Engineering, or Data Science.
• Coursework or academic project experience in AI, ML, software development, data structures, or algorithms.
• Strong analytical, problem-solving, and communication skills.

Responsibilities

• Support experimentation, research, and PoC work related to AI, ML and data science technologies.
• Assist in design, development, testing, and support of AI and data-driven software solutions.
• Collaborate with cross-functional team members to understand business needs and translate them into technical tasks.
• Document applications and prototypes and contribute to knowledge sharing.

Skills

Python

Java

JavaScript

Version control

Debugging

Testing

Communication

Education

Bachelor’s or Master’s degree in CS/AI/Software Engineering/Data Science

Tools

Git

Job description

Manhattan Associates in Boston, MA, seeks a motivated AI Developer Co-op currently pursuing a degree in Computer Science, AI, or Data Science. You will apply classroom knowledge to real-world supply chain AI problems, building, testing, and improving AI-driven solutions alongside experienced developers.

You’ll work full-time, collaborate with engineers and business partners, and gain hands-on experience across experimentation, data processing, and documentation in a supportive, inclusive