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Principal Engineers, Software located in Bellevue, WA will detect and resolve problems by analyzing large amounts of data, defining new metrics and business cases, designing simulations and experiments, creating machine learning (ML) software and models, and collaborating with experts to develop closed-loop analytics and automation software solutions and reporting.
Job Responsibility
Collaborate with software developers and system engineers to design, implement, and deploy software for ML solutions which meet customer requirements, scale easily, and support deployment in highly available environments
Perform datamining and modeling to discover insights, operationalizing models and identifying opportunities through the use of machine learning, algorithmic, and visualization techniques
Analyze large scale telemetry data and build models that predict failure of T-Mobile network assets, radios, FRU and basebands
Ability to think outside the box to develop new algorithms and methods for novel solutions to challenging business problems
Perform research and work with technical teams to implement new and emerging technologies that will facilitate better data integrity, reliability, and enrichment for quantitative solutions
Leverage the business objectives, systems, and data pipelines to operationalize ML in large-scale environments
Solve these problems using appropriate assumptions, methodologies, and current data science best practices, developing necessary software
Work with different teams to identify areas for efficiency improvements
Support ML projects from strategy through implementation and on-going improvements
Apply cost-effective ML tools and techniques to reduce operational ML costs by utilizing tools such as ML.Net, ONNX, or equivalent
Improve neural network model efficiency via parameter and structure tuning using various optimization methods, gird search and evolutionary optimization techniques
Perform data collection, data mining, analysis, validation, and cleansing
Develop software in support of multiple machine learning workflows and integrate and deploy code in large-scale production environments and execute reporting
Develop new and efficient analytical systems in F# and enhance and maintain existing ones
Develop software and dashboards to track application performance and insights
Lead the implementation, assessment, and standardization of toolkits for our data science, measurement science, insight management, and visualization teams
Provide senior-level guidance to the data science and measurement science teams on approaches and methodologies as a subject matter expert
Responsible for technology strategy, which will involve evaluating new and existing technology options that supports business goals
Requirements
Bachelor's degree in Computer Science, or a related field, or the foreign equivalent, and 7 years of related work experience
Master's degree in Computer Science, or a related field, or the foreign equivalent, and 5 years of related work experience
Utilizing knowledge of machine learning algorithms, including Deep Learning, Multilayer Perceptron, Recurrent Neural Networks, Convolutional Networks, Auto Encoders, Variational Auto Encoders, Bayes Point Machine, Deep Semantic Networks, Fast Fourier Transform, Latent Dirichlet Allocation, Random Forest, Randomized PCA, Anomaly Detection, and Gradient Boosted Trees
Deploying at least one of the following models for machine failure prediction: Classification or Anomaly Detection
Utilizing Scala programming with Spark experience to perform complex processing of large-scale telemetry data over distributed clusters
Utilizing F# programming language for constructing robust, high-performance, and efficient analytical systems
Using machine learning model in construction and inference with ML.Net
Experience with model structure and parameter tuning using Grid search and evolutionary optimization methods