Washington Staffing
Machine Learning Engineer
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. 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! Job Overview: The Machine Learning (ML) Engineer focuses on coding, deploying, and maintaining large-scale machine learning models throughout their lifecycle. By combining software engineering principles and data science/machine learning knowledge, the ML Engineer develops the data processes that make ML models generally available for use in products for end-users and customers. The ML engineer should understand machine learning algorithms, have experience in software engineering and various programming languages, including Python, SQL, and Apache Spark. An understanding of latest cloud technologies is imperative for the development and deployment of ML solutions as well. The chief contribution of the ML Engineer is their ability to optimize machine learning solutions for performance and scalability. Job Responsibilities: Build and maintain the entire machine learning lifecycle (research, design, experimentation, development, deployment, monitoring, and maintenance). Assemble large, complex data sets that meet functional/non-functional business requirements for machine learning. Collaborate with data science, engineering, and product teams on defining, architecting, and building data ingestion systems and model training pipelines from experimentation to deployment, monitoring, and continuous performance improvement. Optimize model performance, including feature engineering, hyperparameter tuning, and algorithm selection. Develop, train, test, and evaluate machine learning and deep learning models, involving both traditional ML algorithms (e.g., classification, regression, clustering, SVM) and deep learning architectures (e.g., LSTM, CNNs). Work with large language models and leverage ML frameworks such as Tensorflow, Keras, PyTorch and HuggingFace for model development, testing, and evaluation. Utilize platforms such as Databricks, Snowflake, and Apache Spark to build and manage ML pipelines. Leverage containerization and orchestration tools (Docker, Kubernetes) Stay updated with the latest AI/ML research, tools, and technologies to enhance development practices. Education and Work Experience: Bachelor's Degree in Computer Science, Statistics, Informatics, Information Systems, Machine Learning, or another quantitative field (Required). Master's/Advanced Degree in Computer Science, Statistics, Informatics, Information Systems, Machine Learning, or another quantitative field (Preferred). Experience in developing or deploying ML models in production (Required). Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement (Required). Hands-on experience in programming languages such as Python and/or R and ML Frameworks like TensorFlow, Keras, and PyTorch (Required). Experience in big data platforms like Databricks, Snowflake and Apache Spark (Preferred). Telecom industry experience (Preferred). Knowledge, Skills, and Abilities: Machine Learning & Deep Learning: Solid understanding of classic supervised and unsupervised machine learning algorithms (e.g., classification, clustering, regression, SVM) (Required). Hands-on experience with model training, testing, and evaluation techniques. (Required). Familiarity with modern ML frameworks (TensorFlow, Keras, PyTorch, TensorBoard, Hugging Face) for model development and testing (Required). Experience with deep learning models (LSTM, CNN) and LLMs (Required)
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. 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! Job Overview: The Machine Learning (ML) Engineer focuses on coding, deploying, and maintaining large-scale machine learning models throughout their lifecycle. By combining software engineering principles and data science/machine learning knowledge, the ML Engineer develops the data processes that make ML models generally available for use in products for end-users and customers. The ML engineer should understand machine learning algorithms, have experience in software engineering and various programming languages, including Python, SQL, and Apache Spark. An understanding of latest cloud technologies is imperative for the development and deployment of ML solutions as well. The chief contribution of the ML Engineer is their ability to optimize machine learning solutions for performance and scalability. Job Responsibilities: Build and maintain the entire machine learning lifecycle (research, design, experimentation, development, deployment, monitoring, and maintenance). Assemble large, complex data sets that meet functional/non-functional business requirements for machine learning. Collaborate with data science, engineering, and product teams on defining, architecting, and building data ingestion systems and model training pipelines from experimentation to deployment, monitoring, and continuous performance improvement. Optimize model performance, including feature engineering, hyperparameter tuning, and algorithm selection. Develop, train, test, and evaluate machine learning and deep learning models, involving both traditional ML algorithms (e.g., classification, regression, clustering, SVM) and deep learning architectures (e.g., LSTM, CNNs). Work with large language models and leverage ML frameworks such as Tensorflow, Keras, PyTorch and HuggingFace for model development, testing, and evaluation. Utilize platforms such as Databricks, Snowflake, and Apache Spark to build and manage ML pipelines. Leverage containerization and orchestration tools (Docker, Kubernetes) Stay updated with the latest AI/ML research, tools, and technologies to enhance development practices. Education and Work Experience: Bachelor's Degree in Computer Science, Statistics, Informatics, Information Systems, Machine Learning, or another quantitative field (Required). Master's/Advanced Degree in Computer Science, Statistics, Informatics, Information Systems, Machine Learning, or another quantitative field (Preferred). Experience in developing or deploying ML models in production (Required). Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement (Required). Hands-on experience in programming languages such as Python and/or R and ML Frameworks like TensorFlow, Keras, and PyTorch (Required). Experience in big data platforms like Databricks, Snowflake and Apache Spark (Preferred). Telecom industry experience (Preferred). Knowledge, Skills, and Abilities: Machine Learning & Deep Learning: Solid understanding of classic supervised and unsupervised machine learning algorithms (e.g., classification, clustering, regression, SVM) (Required). Hands-on experience with model training, testing, and evaluation techniques. (Required). Familiarity with modern ML frameworks (TensorFlow, Keras, PyTorch, TensorBoard, Hugging Face) for model development and testing (Required). Experience with deep learning models (LSTM, CNN) and LLMs (Required)