Machine Learning EngineerThis position conducts the design, build, test, and delivery of Machine Learning (ML) models and software components that solve challenging business problems for the organization, working in collaboration with the Business, Product, Architecture, Engineering, and Data Science teams. This position supervises and engages in assessment and analysis of large-scale data sources of structured and unstructured data (internal and external) to uncover opportunities for ML and Artificial Intelligence (AI) automation, predictive methods, and quantitative modeling across the organization. This position designs trials and tests to measure the success of software and systems, and works with teams, or individually, to implement ML/AI models for production scale.

Responsibilities

Transforms and develops data science prototypes into production grade ML and AI agent systems using appropriate datasets and data representation models with moderate complexity.

• 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.
• 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.
• 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.
• 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.

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.

Designs and executes experiments, evaluations, and success metrics for ML models and AI agents, including performance, reliability, accuracy, and business impact.

Implements monitoring, logging, and observability for ML systems and AI agents to ensure ongoing performance, traceability, and continuous improvement.

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.

Collaborates with skilled Designers, Architects, Software Engineers, Data Scientists and Data Engineers to deliver ML products and systems for the organization.

Qualifications

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.

Employee Type: Permanent

UPS is committed to providing a workplace free of discrimination, harassment, and retaliation. UPS is an equal opportunity employer. UPS does not discriminate on the basis of race/color/religion/sex/national origin/veteran/disability/age/sexual orientation/gender identity or any other characteristic protected by law.

Basic Qualifications

Must be a U.S. Citizen or National of the U.S., an alien lawfully admitted for permanent residence, or an alien authorized to work in the U.S. for this employer.