Machine Learning Engineering Manager

Gsfsgroup - Chicago, IL

Full-time - Mid Level

Chicago, IL

5,001-10,000 employees

About the position

There's never been a more exciting time to join United Airlines. We're on a path towards becoming the best airline in the history of aviation. Our shared purpose - Connecting People, Uniting the World - is about more than getting people from one place to another. It also means that as a global company that operates in hundreds of locations around the world with millions of customers and tens of thousands of employees, we have a unique responsibility to uplift and provide opportunities in the places where we work, live and fly, and we can only do that with a truly diverse and inclusive workforce. And we're growing - in the years ahead, we'll hire tens of thousands of people across every area of the airline. Our careers include a competitive benefits package aimed at keeping you happy, healthy and well-traveled. From employee-run "Business Resource Group" communities to world-class benefits like parental leave, 401k and privileges like space available travel, United is truly a one-of-a-kind place to work. Are you ready to travel the world? We believe that inclusion propels innovation and is the foundation of all that we do. United's Digital Technology 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. Our team designs, develops and maintains massively scaling technology solutions brought to life with innovative architectures, data analytics, and digital solutions.

Responsibilities

• Collaborate with data scientists and data engineers to support machine learning needs for commercial and operational projects.
• Design and implement key components of the Machine Learning Platform and business use cases.
• Establish processes and best practices for machine learning.
• Build high-performance, cloud-native machine learning infrastructure and services.
• Develop tools and apps to enable ML automation using the AWS ecosystem.
• Build data pipelines to enable ML models for batch and real-time data.
• Support large scale model training and serving pipelines in a distributed and scalable environment.
• Stay aligned with the latest developments in cloud-native and ML ops/engineering and experiment with new technologies.
• Optimize and fine-tune generative AI/LLM models to improve performance and accuracy.
• Evaluate the performance of LLM models and implement LLMOps processes to manage the end-to-end lifecycle of large language models.

Requirements

• Bachelor's Degree in Computer Science, Engineering, or a related technical field.
• 8+ years of software engineering experience with languages such as Python, Go, Java, Scala, Kotlin, or C/C++.
• 6+ years of experience in machine learning, deep learning, and natural language processing.
• 4+ years of experience working in cloud environments (AWS preferred) - Kubernetes, Dockers, ECS and EKS.
• 2+ years of experience with Big Data technologies such as Spark, Flink and SQL programming.
• 3+ years of experience with cloud-native DevOps, CI/CD.
• 1+ years of experience with Generative AI/LLMs.
• Familiarity with data science methodologies and frameworks (e.g., PyTorch, Tensorflow) and preferably building and deploying production ML pipelines.
• Experience in ML model life cycle development and familiarity with common algorithms like XGBoost, CatBoost, Deep Learning, etc.
• Cloud-native DevOps, CI/CD experience using tools such as Jenkins or AWS CodePipeline; preferably experience with GitOps using tools such as ArgoCD, Flux, or Jenkins X.
• Experience writing, testing, and deploying ML solutions using declarative infrastructure as code solutions: Terraform, Pulumi, AWS CloudFormation, Azure Resource Manager, or GCP Deployment Manager.
• Experience with generative models such as GANs, VAEs, and autoregressive models.
• Experience with LLMOps to manage the end-to-end lifecycle of large language models.
• Prompt engineering skills to design and craft prompts that evoke desired responses from LLMs.

Nice-to-haves

• Master's/PhD degree.

Benefits

• Competitive benefits package including parental leave and 401k.
• Privileges like space available travel.
• Employee-run "Business Resource Group" communities.