Machine Learning Engineering Manager

United Airlines; Chicago, Northern (IL, KY); Hybrid; USD 118,000 - 153,000; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

Life insurance

Disability coverage

Parental leave

Employee assistance program

Commuter benefits

Paid holidays

Paid time off

401(k)

Flight privileges

Job summary

United Airlines is seeking a skilled ML Engineering lead to design and implement components of our Machine Learning Platform. You will work with data scientists, data engineers, and IT to deploy high-performance ML solutions and data-intensive workflows across large-scale computing frameworks.

You will help build reproducible feature pipelines, handle data ingestion and governance, and own production ML workloads with emphasis on CI/CD and automation.

Qualifications

• Bachelor’s degree in computer science, engineering, or related technical discipline.
• 3+ years of experience in managing technical teams and projects.
• 3+ years of experience in full software lifecycle development using Python.
• 3+ years of experience leading an ML Ops team familiar with large cloud environments, Big Data technologies.
• 3+ years in software development in Python, Java, PySpark.
• 3+ Years of Experience with Machine Learning and Machine Learning workflows.
• 3+ years of experience designing and developing using Docker, Kubernetes.
• Strong software engineering experience with Python and at least one additional language.
• Understanding of machine learning principles and techniques.
• Experience with data science tools and frameworks (e.g. PyTorch, Tensorflow, Keras, Pandas, Numpy, Spark).
• Experience designing and developing scalable cloud-native solutions using Docker, Kubernetes, AWS Lambda/EKS/ECS/Fargate.
• Experience building infrastructure-as-code templates (e.g. CloudFormation) and CI/CD pipelines (CodePipeline).
• Experience building ETL pipelines and working with big data technologies (Hadoop, Spark, EMR, Redshift, S3, AWS Glue, Kinesis).
• Knowledge of distributed systems for compute and data storage.

Responsibilities

• Design and implement key components of the Machine Learning Platform infrastructure and establish processes and best practices.
• Collaborate with data scientists, data engineers, and IT to deploy high-performance ML solutions and data pipelines.
• Create reproducible feature pipelines to train models and serve features in production.
• Address data ingestion, pipeline, and governance challenges for ML solutions.
• Own production systems with focus on delivery, CI, and automation of ML workloads.
• Provide mentorship and code reviews to data scientists and ML engineers.

Skills

Python

Java

PySpark

Docker

Kubernetes

ML Ops

Cloud

Big Data

ETL

Education

Bachelor’s degree in CS/Engineering

Tools

AWS CodePipeline

AWS CloudFormation

EMR

Kinesis

Job description

Achieving our goals starts with supporting yours. Grow your career, access top-tier health and wellness benefits, build lasting connections with your team and our customers, and travel the world using our extensive route network.

Come join us to create what’s next. Let’s define tomorrow, together.

Description

Job overview and responsibilities

Develops and programs integrated software algorithms to structure, analyze and leverage data in systems applications. Develops and communicates statistical modeling techniques to develop and evaluate algorithms to improve product/system performance, quality, data management and accuracy. Completes programming and implements efficiencies, performs testing and debugging. Completes documentation and procedures for installation and maintenance. Applies deep learning technologies to give computers the capability to visualize, learn and respond to complex situations. Can work with large scale computing frameworks, data analysis systems and modeling environments.

• Design and implement key components of the Machine Learning Platform infrastructure and establish processes and best practices
• Work cross-functionally with data scientists, data engineers, and IT teams to design, develop, deploy, and integrate high-performance, production-grade machine learning solutions and data intensive workflows
• Partner with data scientists and data engineers to create and refine features from underlying data and build reproducible feature pipelines to train models and serve features in production
• Partner with data platform and operations teams to solve complex data ingestion, pipeline and governance problems for machine learning solutions
• Take ownership of production systems with a focus on delivery, continuous integration, and automation of machine learning workloads
• Provide technical mentorship, guidance, and quality-focused code review to data scientists and ML engineers

Qualifications

What’s needed to succeed (Minimum Qualifications):

• Bachelor’s degree in computer science, engineering, or a related technical discipline
• 3+ years of experience in managing technical teams and projects
• 3+ years of experience in full software lifecycle development using Python
• 3+ years of experience leading an ML Ops team familiar with large cloud environments, Big Data technologies
• 3+ years in software development in Python, Java, PySpark
• 3+ Years of Experience with Machine Learning and Machine Learning workflows
• 3+ years of experience designing and developing using technologies as Docker, Kubernetes
• Strong software engineering experience with Python and at least one additional language such as Java, Go, Rust, or C/C++
• Understanding of machine learning principles and techniques
• Experience with data science tools and frameworks (e.g. PyTorch, Tensorflow, Keras, Pandas, Numpy, Spark)
• Experience designing and developing scalable cloud native solutions using technologies such as Docker and Kubernetes and serverless services such as AWS Lambda, EKS, ECS, Fargate
• Experience building infrastructure-as-code templates (e.g. AWS CloudFormation) and cloud-native CI/CD pipelines using tools such as AWS CodePipeline
• Experience building ETL pipelines and working with big data technologies (e.g. Hadoop, Spark, and serverless technologies such as EMR, Redshift, S3, AWS Glue, and Kinesis)
• Knowledge of distributed systems as it pertains to compute and data storage
• Strong desire to experiment with and learn new technologies and stay aligned with the latest community developments in ML Ops/Engineering and cloud native
• Excellent oral and written communication skills. Ability to prepare high-quality presentation materials and explain complex concepts and technical materials to less-technical audiences
• Must be legally authorized to work in the United States for any employer without sponsorship
• Successful completion of interview required to meet job qualification
• Reliable, punctual attendance is an essential function of the position

What will help you propel from the pack (Preferred Qualifications):

• AWS Certified Solution Architect (Associate or Professional)
• Experience working as a Machine Learning Engineer or Data Scientist building and productional machine learning solutions
• Experience building real-time event-driven stream processing solutions with technologies such as Kafka, Flink, and Spark
• Experience with GPU acceleration (e.g. CUDA and CuDNN)
• Experience with Kubernetes

The base pay range for this role is $117,610.00 to $153,146.00. The base salary range/hourly rate listed is dependent on job-related, factors such as experience, education, and skills. This position is also eligible for bonus and/or long-term incentive compensation awards.

You may be eligible for the following competitive benefits: medical, dental, vision, life, accident & disability, parental leave, employee assistance program, commuter, paid holidays, paid time off, 401(k) and flight privileges.

United Airlines is an Equal Opportunity Employer. We recruit, employ, train, compensate, and promote without regard to race, color, religion, national origin, gender identity, sexual orientation, disability, age, veteran status, or any other protected category under applicable law. We provide reasonable accommodations for applicants and employees with disabilities. To request an accommodation, contact [e-mail]