Machine Learning Engineer II

The University Corporation for Atmospheric Research is hiring a Machine Learning Engineer II to design and develop machine learning software and systems for Earth System Science applications. The role builds scalable workflows and infrastructure for training, evaluating, deploying, and operating models, and collaborates with scientists and technical experts to deliver reliable computational solutions.

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

Preferred

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

Machine Learning Workflows

Large-Scale Data Processing

High-Performance Computing

Software Development Lifecycle

Requirements Analysis

Software Design

Automated Workflows

Data Preparation

Model Evaluation

Model Inference

Model Deployment

Scientific Software Development

Continuous Integration

Reproducible Software

Collaboration

Technical Team Leadership

Complex Problem-Solving

Benefits

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.

Relocation assistance eligible

Medical, Dental, Vision, and Short & Long Term Disability Insurance

UCAR contributes 10% of base salary to the retirement plan

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

Hybrid work arrangement: approximately 3 days per week on-site

Company

Manages atmospheric research, observing, and computing facilities

Boulder, Colorado, USA

https://www.ucar.edu/

Founded in 1960

1001-5000 employees

Qualifications

Bachelor of Science degree and **4 years of related experience**, or

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 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

Or an equivalent combination of education and experience

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

Required skills

Machine Learning, Model Training, Model Evaluation, Inference, Software Testing, Version Control, Code Review, Reproducible Software, High-Performance Computing, Large-Scale Data Processing, Earth System Science, Continuous Integration

Experience

4+ years