Machine Learning Engineer

UPS Supply Chain Solutions

1d

About The Position

This position participates in designing, building, testing, and delivering machine learning (ML) models and software components that address business problems. It collaborates with Business, Product, Architecture, Engineering, and Data Science teams and analyzes structured and unstructured internal and external data to identify opportunities for ML and AI automation, prediction, and quantitative modeling. The role establishes and configures scalable, cost-effective end-to-end solution components to support prediction model transactions, designs trials and tests to measure software and systems performance, and implements ML/AI models for production use.

The position is located in Atlanta, Georgia. Hours of operation are subject to change; the stated hours are 8:00 AM–5:00 PM EST. Candidates must be located in the same geographic area or be willing to self-relocate. Relocation assistance is not offered, and the employer will not sponsor visas. The role is permanent. Applicants must meet the stated U.S. work authorization criteria.

Requirements

• Experience designing and building data-intensive solutions using distributed computing in a multi-line business environment.
• Familiarity with machine learning and artificial intelligence frameworks such as Keras and PyTorch, libraries such as scikit-learn, and tools and cloud AI technologies that streamline ML or AI system development.
• Experience establishing and configuring scalable, cost-effective end-to-end solution components for serving batch and live-streaming prediction model transactions.
• Creative and critical thinking skills.
• Experience developing classification, regression, NLP, and deep learning models, with a focus on productionizing models into product features.
• Experience with scalable data processing, feature development, and model optimization.
• Solid understanding of statistics, including forecasting, time series, hypothesis testing, classification, clustering, or regression analysis, and their application to evaluating machine learning models.
• Knowledge of the software development lifecycle, Agile development practices, and cloud technology infrastructures and patterns related to product development.
• Advanced mathematics skills in linear algebra, Bayesian statistics, and group theory.
• Ability to work collaboratively in technical and cross-functional contexts.
• Strong written and verbal communication skills.
• Bachelor's degree in a quantitative field such as mathematics, computer science, physics, economics, engineering, or statistics, an international equivalent, or equivalent job experience.
• Must be a U.S. citizen or national, a lawful permanent resident, or otherwise authorized to work in the U.S. for this employer.

Responsibilities

• Transform data science prototypes into machine learning systems using appropriate datasets and data representations.
• Research and implement machine learning algorithms and tools to create systems and processes that meet business requirements.
• Collaborate with others to deliver machine learning products and systems.
• Design workflows and analysis tools to streamline development of machine learning models at scale.
• Create and evolve machine learning models and software for intelligent systems, applying engineering and modeling lifecycle best practices.
• Extend existing machine learning libraries and frameworks with developments in data science and machine learning.
• Establish, configure, and support scalable cloud components that serve prediction model transactions.
• Integrate authoritative internal and external data sources to form the foundation of data products that provide insights supporting business outcomes and machine learning systems.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level