Application Reader

AI4

ALL

Application Reader

Seasonal, Part-Time;
Remote

AI4

ALL is a national nonprofit that is transforming the pipeline of AI practitioners and creating a more inclusive, human-centered discipline. We empower students to be AI Changemakers by cultivating an environment where they develop skills in critical thinking and relationship-building, and expertise in responsible AI.

To accomplish this, we seek students from communities that have been excluded in the AI space. Then we help them build identity in the field, and tap into their own agency and initiative as they build technical and leadership skills. While all are welcome to apply, AI4

ALL prioritizes students whose race, gender, or ethnicity has been historically excluded from AI:
Black, Hispanic and Latinx, and Indigenous folks; and women and non-binary folks. Our programs run in partnership with colleges and universities around North America.

We are launching the diverse next generation of AI changemakers through education, ethics, and relationship-driven networks. AI4

ALL has received funding from Melinda Gates/Pivotal Ventures, Prudential Financial, Accenture, General Motors, and Soft Bank among others and has been featured in the New York Times, TIME, CNN, The Washington Post, USA Today, and The Atlantic.

We are seeking two seasonal part-time Application Readers to join our team and play a key role in supporting the AI4

ALL Ignite Accelerator admissions process. AI4

ALL Ignite Accelerator is a virtual 20 week comprehensive program designed to equip college students whose race, gender, or ethnicity has been historically excluded from AI with the necessary skills and support to thrive in the AI/ML field.

Reporting to the Program Associate, part-time Application Readers will focus on reviewing student applications within the admissions platform, accurately labeling and organizing applicant data, monitoring application trends and platform performance, and keeping the admissions team informed throughout the cycle. This role will also assist with reviewing confirmation of enrollment submissions, responding to student inquiries, and identifying opportunities to improve the admissions experience.

This role is ideal for a highly organized, tech-savvy, and detail-oriented individual who thrives in a collaborative environment and is committed to AI4

ALL’s mission to transform the pipeline of AI practitioners and create a more inclusive, human-centered discipline.

This seasonal part-time position is fully remote and requires individuals to conduct work from their own computers. The position will run from January 2026 to May 2026 with the possibility for extension. The role requires approximately 10 hours per week. Most hours are asynchronous, but some synchronous hours will be required. Synchronous time may include meetings and training sessions, and will be included in the weekly time commitment.

Application Readers will be responsible for:

Application Management (90%)

• Work within applications system to review incoming student applications and accurately label and organize applicant data
• Provide regular updates to the Admissions team regarding application trends, platform functionality, and other relevant observations impacting the admissions process.
• Review confirmation of enrollment submissions for accepted students and provide regular updates on the results to the Program Associate

Student Admissions (10%)

• Respond to student questions regarding the admissions process or their application status, providing timely and accurate information
• Reach out to students for missing information on their applications or confirmation of enrollment
• Participate in admissions retrospectives and provide feedback to improve processes, resources, and the overall applicant experience.

The Application Reader position calls for a motivated individual who embodies the following knowledge, skills, and experience:

• 1-2 years of professional work experience, ideally working in an admissions role or with Undergraduate students.
• Comfort working with data and online systems (e.g., filtering, sorting, or labeling data; ensuring accuracy and…