Machine Learning Engineering Manager

United Airlines - Chicago, IL

Full-time - Mid Level

Chicago, IL

Air Transportation

About the position

The Machine Learning Engineering Manager at United Airlines will lead the Data and Machine Learning Engineering team, focusing on developing and maintaining cloud-native machine learning infrastructure and services. This role is pivotal in driving data-driven insights and innovations to support commercial and operational projects, collaborating closely with data scientists and engineers to implement best practices and optimize machine learning models.

Responsibilities

• Build high-performance, cloud-native machine learning infrastructure and services to enable rapid innovation across United.
• Design and 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 and deploy them.
• 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 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.

Nice-to-haves

• Master's/PhD degree.

Benefits

• Parental leave
• 401k
• Space available travel privileges
• Competitive benefits package