Machine Learning Engineer II

University Corporation for Atmospheric Research•Boulder, CO

8d•Hybrid

About The Position

UCAR is seeking a Machine Learning Engineer II to independently design and develop advanced machine-learning software and systems for Earth System Science applications. The role develops robust, scalable, maintainable software to train, evaluate, deploy, and operate machine-learning models using large-scale Earth science datasets and high-performance computing resources. The engineer leads technical implementation for projects and collaborates with scientists, software engineers, and domain experts to translate scientific requirements into reliable computational solutions. The position may also contribute to established community tools, frameworks, workflows, and infrastructure. This is a fixed-term role described as a 6-month-or-more position and as a regular, 2-year term position. The hiring range is $113,470–$141,840 USD; final salary and rates depend on relevant education, experience, and skills. Relocation assistance is eligible. The application deadline is November 1, 2026, at 12:00 a.m. MST. The role requires a Bachelor of Science degree and four years of related experience, a master’s degree, or an equivalent combination of education and experience. UCAR offers a benefits package that includes health insurance, dental insurance, retirement, leave plans, tuition assistance, and employee assistance programs. Hybrid staff are expected to work onsite approximately three days per week. All roles require compliance with U.S. export control regulations and work location requirements.

Requirements

Bachelor of Science degree and four years of related experience, a master’s degree, or an equivalent combination of education and experience.
Demonstrated proficiency applying machine-learning methods and frameworks to practical problems, including model training, technical evaluation, and inference.
Proficiency 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.

Nice To Haves

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.

Responsibilities

Design and implement 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.
Develop robust workflows and infrastructure for machine-learning data preparation, model training, evaluation, inference, and deployment.
Develop reusable software tools, libraries, APIs, and workflows that enable researchers and developers to incorporate machine learning into Earth System Science applications.
Collaborate with scientists, software engineers, and domain experts to translate scientific requirements into well-designed, maintainable software solutions.
Analyze and optimize machine-learning application performance, 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.
Maintain current knowledge of machine-learning engineering practices, software frameworks, computing technologies, and approaches relevant to Earth System Science.