Summer 2026 Machine Learning Engineering Internship
T-MOBILE USA, Inc. | |
relocation assistance
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United States, Georgia, Atlanta | |
Jan 30, 2026 | |
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T-Mobile is synonymous with innovation-and you could be part of the team that disrupted an entire industry! We reinvented customer service, brought real 5G to the nation, and now we're shaping the future of technology in wireless and beyond. Our work is as exciting as it is rewarding, so consider the career opportunity below as your invitation to grow with us, make big things happen with us, above all, #BEYOU with us. Together, we won't stop! Get hands-on experience, training-and a leg up on a bright future. Learn. Achieve. Build a Career.T-Mobile is revolutionizing the wireless industry for millions of customers nationwide. Working here means rolling up your sleeves and redefining the status quo with a team that has your back every step of the way! This is a 11-week paid learning experience during which you'll be able to connect and network with other interns and leaders within the company. We invite you to come innovate with mentors who will challenge you to develop meaningful skills. You'll contribute your creativity and outstanding ideas, while working alongside T-Mobile employees. We'll give you hands-on projects and the chance to create an immediate impact. What It's Like: Our team builds AI solutions that improve customer experience and help resolve customer issues efficiently at scale. We focus on AI observability by developing tools and frameworks to monitor, evaluate, and improve machine learning and LLM-based systems. Our scope includes model evaluation (Evals), performance monitoring, and data-driven insights to ensure AI solutions are reliable, trustworthy, and production-ready. By enabling visibility into AI behavior, we help accelerate innovation while delivering smarter and more dependable customer-facing experiences across T-Mobile. What You'll Do: This role will focus on ML engineering projects, building scalable training and inference pipelines, APIs, and service integrations. The work includes designing and running model evaluations (Evals), including offline and online testing, to assess model quality, accuracy, and performance. The role will also contribute to MLOps and reliability efforts by implementing model versioning, CI/CD workflows, and drift detection for production systems. Additionally, the scope includes translating research ideas and early prototypes into production-ready machine learning solutions, accelerating experimentation and deployment across the team. Key Responsibilities:
What It Takes:
Never stop growing! | |
relocation assistance
Jan 30, 2026