Job Description

At T-Mobile, we invest in YOU! Our Total Rewards Package ensures that employees get the same big love we give our customers. All team members receive a competitive base salary and compensation package - this is Total Rewards. Employees enjoy multiple wealth-building opportunities through our annual stock grant, employee stock purchase plan, 401(k), and access to free, year-round money coaches. That’s how we’re UNSTOPPABLE for our employees! Are you ready to join the Un-carrier movement?The Associate Machine Learning (ML) Engineer at T-Mobile is instrumental in advancing our AI-driven initiatives. This role focuses on supporting the design, development, and deployment of machine learning models that enhance our customer interactions and operational efficiency. By leveraging data-driven insights and modern ML frameworks, the engineer contributes to innovation and helps integrate AI technologies into T-Mobile’s products and services.Working closely with senior ML engineers, data scientists, and cross-functional engineering teams, this role provides an opportunity to gain hands-on experience with end-to-end ML pipelines, big data platforms, and cloud technologies while learning industry best practices.We pride ourselves on encouraging a culture of innovation, advocating for agile methodologies, and promoting transparency in all that we do. Join us in embodying the spirit of the 'Un-carrier' and make a tangible impact! Our team is dynamic where no day is the same, and we are diverse and inclusive passionate about growth and transformation. If you're up to the challenge, apply today! Job ResponsibilitiesAssist in designing, developing and refining machine learning models to enhance customer interactions and operational efficiency.Support data preparation, training, testing, and evaluation for ML and deep learning models.Build and maintain data pipelines for large-scale training and inferenceOptimize model performance, including feature engineering, hyperparameter tuning, and algorithm selection.Work with large language models (LLMs) and leverage ML frameworks for training, testing and evaluation.Collaborate with data scientists and engineering teams to integrate ML models into production systems and ensure scalability.