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# Machine Learning Engineer II.

- **Company:** University Corporation For Atmospheric Research
- **Location:** Boulder, CO, United States
- **Salary:** $113,000.0 - $142,000.0
- **Contract:** Temporary contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Application Frameworks, Big Data, Code Review, Continuous Integration, Data Cleansing, Machine Learning, Software Maintenance, Software Tools, Software Engineering, High Performance Computing, Deep Learning, Machine Learning Operations, Software Version Control, Programming Languages
- **Published:** October 17, 2026
- **Apply:** https://www.builtincolorado.com/job/machine-learning-engineer-ii/11722709?handler=ApplyRedirect

## About the Role

* 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 QualificationsKnowledge, 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.

## Description

Designs and develops scalable machine learning software and systems for Earth System Science applications. Responsibilities include training, evaluating, deploying, and optimizing models; building data preparation, inference, and deployment workflows; developing reusable tools, libraries, APIs, and infrastructure; collaborating with scientists and engineers; optimizing computational, memory, I/O, scalability, and GPU performance; and maintaining technical documentation. The role is a two-year fixed-term position requiring a bachelor's degree with four years of related experience or a master's degree., 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., * 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.