Machine Learning Engineer II

ucar; Boulder (CO); On-site; USD 113,000 - 142,000; Full time

Benefits offered by this job

Health insurance

Dental coverage

Retirement benefits

Job summary

UCAR seeks a Machine Learning Engineer II to design and develop advanced ML software for Earth System Science applications. You will train, deploy, and operate ML models using large-scale datasets and HPC resources.

Lead technical implementation and collaborate with scientists and engineers to translate scientific requirements into reliable computational solutions. This Regular, 2-Year Term position offers opportunities to contribute to community tools and workflows while maintaining robust

Qualifications

• Demonstrated proficiency applying machine-learning methods and frameworks to practical problems.
• Proficiency in developing reliable, maintainable software using testing, version control, documentation, and reproducibility practices.
• Ability to translate customer or scientific requirements into scoped technical work and communicate trade-offs.

Responsibilities

• Designing and implementing scalable machine learning software and systems to address complex Earth System Science problems.
• Train, evaluate, and deploy machine learning models and associated software components using modern programming languages, frameworks, and software engineering practices.
• Developing robust workflows and infrastructure for machine learning data preparation, model training, evaluation, inference, and deployment.
• Developing reusable software tools, libraries, APIs, and workflows that enable researchers and developers to incorporate machine learning into Earth System Science applications.
• Collaborating with scientists, software engineers, and domain experts to translate scientific requirements into well-designed, maintainable software solutions.
• Analyzing and optimizing the performance of machine learning applications, including computational efficiency, memory utilization, I/O, scalability, and GPU utilization.
• Develop and maintain technical documentation, examples, and developer- and user-facing resources for machine learning software and systems.
• Maintaining current knowledge of machine learning engineering practices, software frameworks, computing technologies, and approaches relevant to Earth System Science.

Skills

ML proficiency

Software engineering practices

Communication of trade-offs

Education

Bachelor of Science degree and 4 years related experience

Master's Degree

Equivalent combination of education and experience

Job description

Position Type: Term - 6 months or more (Fixed Term)

Visa Sponsored Job: No

Relocation Assistance Eligible: Yes

Hiring Range: 113,470.00 USD - 141,840.00 USD *Final salary and rates are based on education, experience, and skills relevant to the role.

Application Closes at 12:00am MST on: 2026-11-01

About the Role

Great Opportunity! We are seeking a Machine Learning Engineer II.

This role will independently design and develop advanced machine learning software and systems for Earth System Science applications. Develop robust, scalable, maintainable software to train, evaluate, deploy, and operate machine learning models using large-scale Earth science datasets and high-performance computing resources. Leads technical implementation for projects and collaborates with scientists, software engineers, and domain experts to translate scientific requirements into reliable computational solutions. May contribute to developing established community tools, frameworks, workflows, and infrastructure.

This is a Regular, 2-Year Term position.

Key Responsibilities

• Designing and implementing scalable machine learning software and systems to address complex Earth System Science problems.
• Train, evaluate, and deploy machine learning models and associated software components using modern programming languages, frameworks, and software engineering practices.
• Developing robust workflows and infrastructure for machine learning data preparation, model training, evaluation, inference, and deployment.
• Developing reusable software tools, libraries, APIs, and workflows that enable researchers and developers to incorporate machine learning into Earth System Science applications.
• Collaborating with scientists, software engineers, and domain experts to translate scientific requirements into well-designed, maintainable software solutions.
• Analyzing and optimizing the performance of machine learning applications, including computational efficiency, memory utilization, I/O, scalability, and GPU utilization.
• Develop and maintain technical documentation, examples, and developer- and user-facing resources for machine learning software and systems.
• Maintaining current knowledge of machine learning engineering practices, software frameworks, computing technologies, and approaches relevant to Earth System Science.

Education & Experience REQUIRED

• Bachelor of Science degree and 4 years of related experience, or
• Master's Degree;
• or an equivalent combination of education and experience.

Knowledge, Skills, and Abilities REQUIRED

• Demonstrated proficiency applying machine-learning methods and frameworks to practical problems, including model training, technical evaluation, and inference.
• Proficiency in developing reliable, maintainable software using testing, version control, code review, documentation, and reproducibility practices.
• Ability to independently analyze moderately complex AI/ML models, data, software, and computing problems and make sound technical decisions.
• Ability to translate customer or scientific requirements into scoped technical work and communicate trade-offs to varied audiences.

Preferred Qualifications

• Experience implementing and optimizing machine learning workflows for large-scale datasets and high-performance computing environments.
• Experience collaborating effectively with scientists, software engineers, and other technical and domain experts to develop solutions to complex Earth System Science problems.
• Experience leading small technical teams through the software development lifecycle, from requirements and design through implementation, testing, deployment, and maintenance.
• Experience designing and implementing automated workflows for data preparation, model training, evaluation, inference, and deployment.
• Experience with software engineering practices for reliable, maintainable scientific software, including testing, continuous integration, version control, documentation, and reproducibility.

Background checks and/or federal screenings are conducted for candidates selected for hire.

Benefits Overview

UCAR offers a comprehensive benefits package : health insurance, dental, retirem