Senior Engineer - Machine Learning

Description

United's Digital Technology team designs, develops, and maintains massively scaling technology solutions brought to life with innovative architectures, data analytics, and digital solutions.

Our Values: At United Airlines, we believe that inclusion propels innovation and is the foundation of all that we do. Our Shared Purpose: "Connecting people. Uniting the world." drives us to be the best airline for our employees, customers, and everyone we serve, and we can only do that with a truly diverse and inclusive workforce. Our team spans the globe and is made up of diverse individuals all working together with cutting-edge technology to build the best airline in the history of aviation.

With multiple employee-run "Business Resource Group" communities and world-class benefits like health insurance, parental leave, and space available travel, United is truly a one-of-a-kind place to work that will make you feel welcome and accepted. Come join our team and help us make a positive impact on the world.

Job Overview And Responsibilities

United Airlines is seeking talented people to join the Data and Machine Learning Engineering team. The organization is responsible for leading data driven insights & innovation to support the Machine Learning needs for commercial and operational projects with a digital focus. This role will frequently collaborate with data scientists and data engineers. This role will design and implement key components of the Machine Learning Platform, business use cases, and establish processes and best practices.

• Build high-performance, cloud-native machine learning infrastructure and services to enable rapid innovation across United. Set up containers and Serverless platform with cloud infrastructure.
• You will design and develop tools and apps to enable ML automation using AWS ecosystem.
• Build data pipelines to enable ML models for batch and real-time data. Hands on development expertise of Spark and Flink for both real time and batch applications.
• Stay aligned with the latest developments in cloud-native and ML ops/engineering and to experiment with and learn new technologies – NumPy, data science packages like sci-kit, microservices architecture.
• Optimize, fine-tune generative AI/LLM models to improve performance and accuracy and deploy them.
• Evaluate the performance of LLM models, Implement LLMOps processes to manage the end-to-end lifecycle of large language models.