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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.
Job Responsibility:
Assist 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 inference
Optimize 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
Utilize platforms such as Databricks, Snowflake, and Apache Spark to build and manage ML pipelines
Support the development of end-to-end model training pipelines using TensorFlow, Keras, PyTorch, HuggingFace and TensorBoard for visualization
Leverage containerization and orchestration tools (Docker, Kubernetes)
Stay updated with the latest AI/ML research, tools, and technologies to enhance development practices
Requirements:
Bachelor's Degree in Computer Science, Engineering, Statistics, Informatics, Information Systems, Machine Learning, or another quantitative field (Required)
Master's/Advanced Degree in Computer Science, Machine Learning, Data Science, or a related field (Preferred)
Experience in developing or deploying ML models (academic, internship, or professional experience acceptable)
Experience with cloud technologies for model training and deployment (Preferred)
Solid understanding of classic supervised and unsupervised machine learning algorithms (e.g., classification, clustering, regression, SVM) (Required)
Familiarity with deep learning architectures (LSTM, CNNs) and LLMs (Preferred)
Knowledge of ML frameworks like TensorFlow, Keras, and PyTorch as well as MLOps tools (Preferred)
Proficiency in data manipulation and analysis using Apache Spark, Databricks, and Snowflake for big data processing (Required)
Working knowledge of SQL for querying and managing databases (Preferred)
Experience with containerization and orchestration tools (Docker, Kubernetes) (Preferred)
Proficiency in Python or R (Required)
Strong knowledge of software engineering principles: version control, testing, CI/CD. (Preferred)
Familiarity with Agile practices for iterative development (Preferred)
Strong foundation in probability, statistics, and mathematics (Required)
Ability to work in cross-functional teams to integrate AI technologies into production (Required)
Strong problem-solving and analytical skills to troubleshoot ML solutions (Required)
Excellent communication skills to collaborate with technical and non-technical teams (Preferred)
At least 18 years of age
Legally authorized to work in the United States
Nice to have:
Master's/Advanced Degree in Computer Science, Machine Learning, Data Science, or a related field
Experience with cloud technologies for model training and deployment
Familiarity with deep learning architectures (LSTM, CNNs) and LLMs
Knowledge of ML frameworks like TensorFlow, Keras, and PyTorch as well as MLOps tools
Working knowledge of SQL for querying and managing databases
Experience with containerization and orchestration tools (Docker, Kubernetes)
Strong knowledge of software engineering principles: version control, testing, CI/CD
Familiarity with Agile practices for iterative development
Excellent communication skills to collaborate with technical and non-technical teams