Senior Machine Learning Engineer

UPS - Louisville, KY

Full-time - Mid Level

Louisville, KY

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About the position

The Senior Machine Learning Engineer at UPS is a pivotal role that involves the design, development, testing, and delivery of machine learning (ML) models and software components aimed at solving complex business challenges. This position requires collaboration with various teams, including Business, Product, Architecture, Engineering, and Data Science, to ensure that the ML solutions align with organizational goals. The engineer will engage in the assessment and analysis of large-scale data sources, both structured and unstructured, to identify opportunities for automation through ML and Artificial Intelligence (AI). In this role, the engineer will be responsible for designing trials and tests to evaluate the effectiveness of software and systems, and will work both independently and as part of a team to implement ML/AI models at a production scale. The position demands a strong understanding of data science principles and the ability to transform prototypes into fully functional ML systems using appropriate datasets and data representation models. The engineer will also research and implement suitable ML algorithms and tools to create innovative systems and processes that leverage ML and AI technologies. Additionally, the role involves creating workflows and analysis tools to facilitate the development of new ML models, ensuring that best practices are followed throughout the engineering and modeling lifecycles. The engineer will extend existing ML libraries and frameworks to enhance enterprise capabilities and will integrate data from various authoritative sources to develop new data products that provide actionable insights for the business.

Responsibilities

• Transforms and develops data science prototypes into ML systems using appropriate datasets and data representation models with moderate complexity.
• Researches and implements appropriate ML algorithms and tools that create new systems and processes powered with ML and AI tools and techniques according to business requirements.
• Designs and implements workflows and analysis tools to streamline the development of new ML models at scale both in batch and streaming mode.
• Creates and evolves ML models and software that enable state-of-the-art intelligent systems using best practices in all aspects of engineering and modeling lifecycles.
• Extends existing ML libraries and frameworks with the developments in the Data Science and ML field for enterprise use.
• Establishes, configures, and supports scalable cloud components that serve prediction model transactions.
• Integrates data from authoritative internal and external sources to form the foundation of a new Data Product that would deliver insights that supports business outcomes necessary for ML systems.
• Collaborates with skilled Designers, Architects, Software Engineers, Data Scientists, and Data Engineers to deliver ML products and systems for the organization.

Requirements

• Experience designing and building large/data-intensive solutions using distributed computing within a multi-line business environment.
• Knowledgeable in Machine Learning and Artificial Intelligence frameworks (i.e., Keras, PyTorch), libraries (i.e., scikit-learn), and tools and Cloud-AI technologies that aid in streamlining the development of machine learning or AI systems.
• Strong experience in establishing and configuring scalable and cost-effective end-to-end solution design pattern components to support the serving of batch and live streaming prediction model transactions.
• Experience in developing and implementing Machine Learning models such as: Classification/Regression Models, NLP models, and Deep Learning models; with a focus on productionizing those models into product features.
• Experience deploying highly scalable software, scalable feature pipeline and model optimization that is supporting millions of transactions and/or substantial number of users.
• Experience in creating products and services that leverage best practices around software development lifecycle (SDLM), Agile development and cloud technology.
• Solid understanding of statistics such as forecasting, time series, hypothesis testing, classification, clustering, or regression analysis, and how to apply that knowledge in understanding and evaluating Machine Learning models.
• Advanced math skills in Linear Algebra, Bayesian Statistics, Group Theory and Probability.
• Works collaboratively with management, and, in a technical and cross-functional context.
• Strong written and verbal communication skills.
• Possesses creative and critical thinking skills.
• Bachelors' (BS/BA) degree in a quantitative field of mathematics, computer science, physics, economics, engineering, statistics (operations research, quantitative social science, etc.), international equivalent, or equivalent job experience.

Nice-to-haves

• Masters (MS/ME) degree in a quantitative discipline such as engineering, computer science, data science, bioinformatics, statistics, mathematics, international equivalent, or equivalent job experience.
• Experience designing and building data-intensive solutions using distributed computing within a multi-line business environment.
• Experience in establishing and configuring scalable and cost-effective end-to-end solution design pattern components to support the serving of batch and live streaming prediction model transactions in the Google Cloud Platform (GCP).
• Experience with scalable data processing, feature development, and model optimization.
• Knowledgeable in software development lifecycle (SDLM), Agile development practices and cloud technology infrastructures and patterns related to product development.
• Advanced math skills in Linear Algebra, Bayesian Statistics, Group Theory.
• Works collaboratively, both in a technical and cross-functional context.
• Prior experience within a UPS operational context (e.g., air gateways, hubs, ground facilities) that can aid with understanding business domain problems.