Ups Digital Senior Machine Learning Engineer

UPS UPS · Logistics · ALPHARETTA, GA +1

Senior Machine Learning Engineer at UPS responsible for designing, building, and deploying ML models and AI agent systems. This role involves transforming prototypes into production-grade systems, implementing end-to-end workflows, and ensuring scalability and observability of AI solutions. The engineer will also extend ML libraries, integrate data sources, and collaborate with cross-functional teams to deliver AI products that solve business problems.

What you'd actually do

  1. Transforms and develops data science prototypes** **into production grade ML and AI agent systems using appropriate datasets and data representation models with moderate complexity.
  2. Researches, evaluates, 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
  3. Designs and implements** **end-to-end ML and AI agent workflows and analysis tools to streamline the development of new ML models at scale both in batch and streaming mode.
  4. 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.
  5. Designs, builds, and maintains AI agent systems that autonomously or semi‑autonomously execute multi‑step tasks, interact with enterprise data, APIs, tools, and other agents, and support human‑in‑the‑loop decision‑making where appropriate.

Skills

Required

  • Experience designing and building large/data-intensive solutions using distributed computing within a multi-line business environment.
  • Knowledgeable in Machine Learning and Artificial Intelligence, and Generative AI 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 designing and implementing AI agent or agent‑like systems, including task orchestration, tool usage, prompt engineering, workflow automation, and integration with enterprise systems.
  • 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 leverages 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
  • 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.

What the JD emphasized

  • production scale
  • production grade
  • productionizing those models into product features
  • AI agent systems
  • AI agent or agent‑like systems

Other signals

  • design, build, test, and delivery of Machine Learning (ML) models and software components
  • implement ML/AI models for production scale
  • Designs and implements end-to-end ML and AI agent workflows
  • Designs, builds, and maintains AI agent systems
  • Extends existing ML libraries and frameworks
  • Establishes, configures, and supports scalable cloud components that serve prediction model transactions
  • Designs and executes experiments, evaluations, and success metrics for ML models and AI agents
  • Implements monitoring, logging, and observability for ML systems and AI agents
  • 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 that is necessary for ML systems
  • Experience designing and implementing AI agent or agent‑like systems, including task orchestration, tool usage, prompt engineering, workflow automation, and integration with enterprise systems.