Machine Learning Educational Trainer

University Corporation for Atmospheric Research (UCAR); Boulder (CO); Híbrido; USD 91 000 - 114 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Health insurance

Retirement plan

Tuition assistance

Relocation assistance

Paid time off

Public Service Loan Forgiveness access

Resumo da oferta

UCAR is seeking a Machine Learning Educational Trainer to develop and deliver AI/ML training tailored to Earth system science. You will design materials, curate resources, and evaluate impact across NSF NCAR programs.

The role collaborates with EdEC teams, coordinates events, and contributes to reports and publications, supporting workforce development in AI/ML.

Qualificações

• Master's degree or equivalent combination with relevant experience in education, training or STEM.
• Bachelor's degree plus eight years of experience or higher.
• Evidence of training design, development, and delivery.
• Experience with AI/ML topics is a plus.

Responsabilidades

• Create and deliver interactive AI/ML training focused on Earth system science.
• Develop accessible instructional materials for diverse learners.
• Coordinate training events, bootstrap camps, and workshops; manage logistics and scheduling.
• Evaluate training impact and report on program outcomes.
• Represent NCAR EdEC in AI program activities and community engagements.

Conhecimentos

Communication Skills

Organization & Initiative

Technical Proficiency

Operations & Compliance

Travel & Logistics

Formação académica

Master's degree in education/ instructional design/ STEM/ CS

Bachelor's degree + 8 years experience

Ferramentas

MS Word

Excel

PowerPoint

Access

Descrição da oferta de emprego

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

Visa Sponsored Job: No

Relocation Assistance Eligible: Yes

Hiring Range: 91,340.00 USD - 114,170.00 USD

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

Application Closes at 12:00am MST on: 2026-10-31

About the Role

UCAR is excited to announce the job opening for the Machine Learning Educational Trainer role. This position is part of the EdEC team and also works directly with the NSF NCAR AI Program. The position will coordinate and conduct staff training in ML and AI, develop Earth system science specific ML and AI staff training material, and curate existing resources. The position will work as part of a larger team focused on ML and AI innovation at NSF NCAR. They support evaluation of staff training impact and contribute to reports and publications about NSF NCAR's AI activities.

NSF NCAR Education, Engagement & Early-Career Development (EdEC) leads the education mission of NSF NCAR by engaging, training, and providing educational development opportunities for the next generation of scientists and engineers, the broader Earth system science community, and the general public. We prepare students and early-career scientists for careers to work in cutting-edge fields such as severe weather observation and modeling, space weather prediction, and new technologies like artificial intelligence and machine learning.

This will be a two-year termed position.

This position is eligible for up to $3500 in relocation assistance.

Job Description

Content Creation and Training

Creates, designs, and delivers interactive professional development and training on AI and ML, with a strong emphasis on Earth system science applications and the needs of NSF NCAR staff. Develops, adapts, and curates accessible instructional materials, examples, and resources for learners with varied levels of AI/ML expertise and diverse range of scientific domains. Incorporates emerging tools, technologies, and best practices into training. Evaluates training effectiveness through participant feedback, participation data, and other measures, and uses findings to continuously improve programming and demonstrate impact.

Coordination

Develops and coordinates technical training activities in support of the NSF NCAR AI Program. Works across teams and with subject-matter experts to identify technical training needs and priorities, coordinates instructors and contributors, and organizes AI/ML learning opportunities for scientific applications. Supports integration of AI/ML staff training with broader NSF NCAR workforce development and community-building activities. Helps plan and manage boot camps, hackathons, workshops, seminars, and other AI/ML events, including logistics, communications, scheduling, and participant engagement.

Representation

Represents NCAR EdEC in NSF NCAR AI Program activities, working groups, events, and community engagements related to AI/ML workforce development. Builds connections among researchers, technical experts, educators, and professional development staff to support the adoption of AI capabilities and foster a collaborative community of practice across NSF NCAR.

Dissemination of Results

Writes and contributes to reports, presentations, articles, and other communications documenting NSF NCAR's AI/ML training and workforce development activities. Synthesizes participation, evaluation, and impact data to communicate outcomes and lessons learned. Develops materials that highlight successful approaches, resources, and opportunities for the broader NSF NCAR and Earth system science communities.

Required Qualifications

• Master's degree in a relevant discipline (e.g., education, instructional design, STEM, computer science, or related field) plus three years of relevant experience; or Bachelor's degree plus eight years of relevant experience; or an equivalent combination of education and experience.

Preferred Qualifications

Preferred Knowledge, Skills, and Abilities

• Communication & Interpersonal Skills: Strong written and oral communication, editing, proofreading, and professional etiquette, combined with collaborative teamwork across diverse backgrounds and the ability to work independently.


• Organization & Initiative: Advanced organizational skills, multi-tasking abilities, and a demonstrated capacity for initiative and independent problem-solving.


• Technical Proficiency: Advanced skills in standard PC applications (Microsoft Word, Excel, PowerPoint, Access), Google Apps, databases, and network computing environments.


• Operations & Compliance: A thorough understanding of policies, procedures, and practices, with the ability to reliably handle confidential information and assess unique situations appropriately.


• Travel & Logistics: Advanced knowledge of travel logistics, planning, and related administrative procedures.

Application Requirements

Cover Letter, Questionnaire, Resume/CV

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.