Machine Learning Engineer II — Earth Systems (Hybrid)

University Corporation for Atmospheric Research; United States; Hybrid; USD 113,000 - 142,000; Full time

Benefits offered by this job

Health insurance

Dental

Retirement plan

Leave plans

Tuition assistance

Employee assistance programs

Public service loan forgiveness (PSLF)

Job summary

UCAR is seeking a Machine Learning Engineer II to design and develop advanced ML software for Earth System Science applications. You will train, evaluate, deploy, and operate models on large-scale datasets using HPC resources, and collaborate with scientists and engineers to translate scientific requirements into reliable software solutions.

The role requires building reusable tools, APIs, and workflows, and maintaining documentation and best practices for scalable ML systems across projects.

Qualifications

• Demonstrated proficiency applying machine-learning methods to practical problems.
• Proficiency in developing reliable, maintainable software with testing and documentation.
• Ability to analyze AI/ML models and data to make sound technical decisions.

Responsibilities

• Design scalable machine learning software and systems for Earth System Science.
• Train, evaluate, and deploy ML models using large datasets and HPC resources.
• Develop robust workflows for data preparation, training, evaluation, inference, and deployment.
• Create reusable tools, libraries, APIs, and workflows to integrate ML into applications.
• Collaborate with scientists, engineers, and domain experts to translate requirements into software.
• Analyze and optimize performance including GPU utilization and scalability.
• Develop and maintain technical documentation and developer resources.

Skills

Machine Learning

Software Development

Collaboration

Education

Bachelor’s degree + 4 years experience or Master’s

Job description

UCAR is seeking a Machine Learning Engineer II to design and develop advanced ML software for Earth System Science applications. You will train, evaluate, deploy, and operate models on large-scale datasets using HPC resources, and collaborate with scientists and engineers to translate scientific requirements into reliable software solutions.

The role requires building reusable tools, APIs, and workflows, and maintaining documentation and best practices for scalable ML systems across projects.