Machine Learning Engineer II

Position Type:

Visa Sponsored Job:

Relocation Assistance Eligible:

Hiring Range:

*Final salary and rates are based on education, experience, and skills relevant to the role.

Application Closes at 12:00am MST on:

About the Role

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.

Job Description

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

Knowledge, Skills, and Abilities Desired, not Required:

• 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.

Application Requirements

For full consideration, all application materials must be submitted by 12am (MST) at the start of the application deadline. These should highlight how your skills and experiences align with the responsibilities of this position. We recognize that no candidate will meet 100% of the listed qualifications. If you believe your background has prepared you for success in this role, we encourage you to apply.

Supplemental questionnaires are often requested in lieu of a cover letter, where you address critical skills needed for the position. Your answers will be read and weighed equally to your resume and should provide specific, detailed, and informative responses based on your direct and previous work experiences.

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

Benefits Overview

UCAR offers a comprehensive benefits package: health insurance, dental, retirement plan, leave plans, tuition assistance, and employee assistance programs:

• Medical, Dental, Vision, and Short & Long Term Disability Insurance
• Generous retirement plan - UCAR contributes of 10% of base salary
• Time Off Plans
  • Minimum 20 days of annual PTO
  • 10 paid holidays
  • 12 weeks of paid parental leave
  • FAMLI Leave
• Tuition Assistance Program
• Public Service Loan Forgiveness (PSLF) eligible employer

UCAR is an Equal Opportunity Employer and welcomes applications from all individuals. We do not discriminate on the basis of race, age, sex, gender identity, disability, veteran status, or any other characteristic protected by law. Applicants are not required to provide age-related information (like date of birth or graduation dates) in initial submissions.

For sponsored positions: UCAR can sponsor individuals for H-1B status only if they are already in the U.S. in H-1B status or another visa status that allows them to change to H-1B status within the U.S. UCAR is unable to sponsor new H-1B visas for individuals who are outside the U.S. and need to obtain H-1B status through Consular Processing.

While UCAR does not prohibit the use of AI tools in job applications, we strongly recommend that applicants submit primarily original work.

All roles require compliance with U.S. export control regulations and work location requirements. All hybrid staff are expected to work on-site approximately 3 days per week.