Summer 2027 AI Engineering Internship

T-Mobile•Bellevue, WA

About The Position

T-Mobile’s Summer 2027 AI Engineering Internship is an 11-week paid learning experience focused on the intersection of software engineering, large language models, and practical workflow automation. The intern will help explore how AI agents and developer tools can improve software engineering workflows at T-Mobile, with work potentially spanning agent integrations, developer experience, testing, and evaluation. The role emphasizes reliable, maintainable solutions and evidence-based assessment of where AI is useful and where human judgment remains essential. The intern will collaborate with engineers to turn real workflow problems into working prototypes, receive mentorship, and contribute ideas with practical impact. The program includes hands-on project work, opportunities to build relationships with interns and leaders, and a final demonstration and handoff of the resulting prototype. Intern wages range from $27 to $50 per hour; actual pay depends on education and STEM versus non-STEM designation. Relocation assistance is available to program participants who live more than 50 miles from the internship location.

Requirements

Foundational programming skills in Python and/or TypeScript/JavaScript, demonstrated through coursework, research, personal projects, or equivalent experience.
Familiarity with Git, APIs, debugging, and automated testing.
Interest in LLMs, AI agents, and developer experience, and willingness to test assumptions and critically review AI-generated code and outputs.
Ability to break down ambiguous problems, communicate clearly, and collaborate on feedback.
At least 18 years of age.
Legally authorized to work in the United States now and in the future.
Must have one or more courses remaining in a bachelor's or graduate degree program after the internship.

Nice To Haves

Exposure to agent frameworks, MCP, RAG, evaluation tools, CI/CD, or cloud platforms; helpful but not required.

Responsibilities

Partner with engineers to define a focused use case, requirements, and measurable success criteria.
Build and test an AI agent, agent tools, API integrations, or developer workflow automations using Python and/or TypeScript.
Create evaluation datasets and regression tests to measure quality and identify failure modes.
Add logging and tracing to investigate errors, latency, and tool behavior while following data-handling and security requirements.
Compare results against a baseline to assess correctness, usefulness, and failure modes.
Document implementation choices and design decisions, share progress, incorporate feedback, and deliver a working demonstration and handoff with clear recommendations for next steps.