Earth System ML Engineer II: Scalable Models

University Corporation for Atmospheric Research (UCAR); Boulder (CO); Hybrid; USD 113,000 - 142,000; Full time

Benefits offered by this job

Health insurance

Retirement plan

Paid time off (PTO)

Tuition assistance

Public Service Loan Forgiveness (PSLF)

Job summary

University Corporation for Atmospheric Research (UCAR) is seeking a Machine Learning Engineer II to design and develop advanced ML software for Earth System Science. You will train, evaluate, deploy, and operate models using large-scale datasets on HPC resources, and lead technical implementation across teams.

The role is a Regular, 2-Year Term with on-site work location and a possible hybrid arrangement. Strong software engineering and ML deployment experience are essential for success.

Qualifications

• BS in a relevant field with 4+ years of related experience, or
• MS degree or an equivalent combination of education and experience.
• Experience applying ML methods to real-world problems and deploying models.

Responsibilities

• Design and develop scalable ML software and systems for Earth System Science.
• Train, evaluate, deploy, and operate ML models using large datasets and HPC resources.
• Lead technical implementation for projects and collaborate with scientists and engineers.
• Translate scientific requirements into reliable computational solutions.
• Develop documentation, examples, and workflows for ML software.

Skills

Machine learning

Software engineering

Python

C++

GPU computing

Model deployment

Version control

Education

Bachelor's Degree in a related field

Master's Degree

Tools

TensorFlow

PyTorch

Keras

scikit-learn

Git

DVC

Job description

University Corporation for Atmospheric Research (UCAR) is seeking a Machine Learning Engineer II to design and develop advanced ML software for Earth System Science. You will train, evaluate, deploy, and operate models using large-scale datasets on HPC resources, and lead technical implementation across teams.

The role is a Regular, 2-Year Term with on-site work location and a possible hybrid arrangement. Strong software engineering and ML deployment experience are essential for success.