Machine Learning Engineer

T-Mobile US-posted 4 months ago
$104,800 - $189,100/Yr
Full-time • Entry Level
Bellevue, WA
Telecommunications
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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.

  • 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.
  • Bachelor's Degree in Computer Science, Statistics, Informatics, Information Systems, Machine Learning, or another quantitative field (Required).
  • 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).
  • Working SQL knowledge and experience working with relational databases, query authoring (SQL), as well as working familiarity with a variety of databases (Required).
  • Working knowledge of modern cloud platforms and deployment pipelines (Required).
  • Master's/Advanced Degree in Computer Science, Statistics, Informatics, Information Systems, Machine Learning, or another quantitative field (Preferred).
  • Experience in big data platforms like Databricks, Snowflake and Apache Spark (Preferred).
  • Telecom industry experience (Preferred).
  • Strong knowledge of software engineering principles: version control, testing, CI/CD (Preferred).
  • Familiarity with Agile practices for iterative development (Preferred).
  • Medical, dental and vision insurance.
  • Flexible spending account.
  • 401(k) with company match.
  • Employee stock grants and employee stock purchase plan.
  • Paid time off and up to 12 paid holidays.
  • Paid parental and family leave.
  • Family building benefits.
  • Back-up care and enhanced family support.
  • Childcare subsidy.
  • Tuition assistance and college coaching.
  • Short- and long-term disability insurance.
  • Voluntary AD&D coverage, voluntary accident coverage, voluntary life insurance, voluntary disability insurance, and voluntary long-term care insurance.
  • Mobile service & home internet discounts.
  • Pet insurance.
  • Access to commuter and transit programs.
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