Unpaid AI Data Annotation Intern

Skipit

RemoteCareer-pivot friendly

Why this role

Pace

Steady

The role operates at a steady pace, with interns required to dedicate approximately 5 to 10 hours per week, allowing for consistent but flexible engagement with the assigned content.

Collaboration

Low

While the role is largely independent, interns will participate in weekly check-ins with the CTO, fostering a collaborative environment for feedback and calibration.

Autonomy

Medium

Interns have significant autonomy in their work, as they are responsible for watching content and making annotations based on their observations, with guidance from the CTO.

Decision Impact

Individual

Decisions made during the annotation process directly impact the quality and effectiveness of the AI models, underscoring the importance of precision and consistency in the work.

Role Level

Individual Contributor

The role requires a nuanced understanding of trigger content and the ability to accurately annotate scenes, making it moderately complex and requiring attention to detail.

Career Pivot Friendly

Welcomes transferable skills

Individuals with backgrounds in content analysis, media studies, or even customer service roles that involve detailed observation and reporting can transition well into this role, leveraging their skills in content evaluation and structured reporting.

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

Contribute to the development of a proprietary AI dataset

Typical background

Any major or background

Transferable backgrounds

Coming from Content analyst at a media company
content analysis · scene evaluation
The ability to analyze and evaluate scenes for specific content types is directly applicable to the role's requirement of identifying and annotating trigger scenes.
Coming from Technical writer in a tech startup
technical writing · structured reporting
Experience in technical writing and providing structured reports can be leveraged to accurately log timestamps, category labels, and descriptive notes for each flagged scene.

Skills & requirements

Required

Attention To DetailWritten Communication

Preferred

AI And Machine Learning Knowledge

Stack & domain

AIMachine LearningData AnnotationWritten CommunicationAttention To DetailConsistencyMental HealthMediaAI Product Development

About the role

As an AI Data Annotation Intern at Skipit, you'll meticulously analyze films and TV shows to flag and annotate scenes with potential triggers, contributing to a unique AI dataset that prioritizes viewer mental health and control.

Original posting from Skipit via LinkedIn

Remote | Unpaid | Part-Time | Rolling Applications
About Skipit
Skipit is a trauma-informed AI streaming overlay that gives viewers control to skip, mute, or receive scene summaries for distressing content as they watch. We are building one of the most meaningful and differentiated datasets in consumer AI: a scene-level trigger intelligence library, hand-annotated with precision and care. This internship is a direct contribution to that dataset.
The Role
As an AI Data Annotation Intern, you will watch films and television shows and flag scenes containing potential emotional or psychological triggers. For each flagged scene, you will log a timestamp, a category label, and a brief description of the content. Your annotations feed directly into the AI models that power Skipit's core product.
This is not a passive task. You will be learning how training data shapes AI behavior, why annotation quality and consistency matter, and what it takes to build a proprietary dataset from the ground up. You will work under the mentorship of our CTO, Jim Jin (prev. BU CS, AI/ML), who will provide guidance on annotation methodology, quality review, and the broader AI systems context.
What You Will Do
Watch assigned films and TV episodes and annotate scenes containing triggers (e.g., violence, substance use, self-harm themes, sudden loud content, etc.)
Log timestamps, trigger category labels, and short descriptive notes for each flagged scene
Submit annotations via a structured format (template provided)
Participate in brief weekly check-ins with the CTO for calibration and feedback
Contribute to refining our annotation taxonomy over time
What You Will Learn
How real-world AI training datasets are built, structured, and quality-controlled
The principles behind scene-level classification and natural language annotation
How annotation decisions affect downstream model behavior
The intersection of mental health, media, and AI product development
Direct mentorship from an AI/ML engineer at a mission-driven early-stage startup
Who We Are Looking For
We welcome applicants from any major or background. You do not need prior technical experience. What matters most is attention to detail, consistency, and genuine interest in the intersection of AI and mental health.
Strong written communication and ability to describe scenes clearly and concisely
Comfort watching content that may include mature or distressing themes (you will not be required to annotate anything you are unwilling to watch)
Reliable availability of approximately 5 to 10 hours per week
Interest in AI, machine learning, mental health tech, or media is a plus
Compensation and Credit
This position is currently unpaid. We are a pre-seed startup and are transparent about that. What we can offer:
A real portfolio contribution: your work goes into a proprietary AI dataset, not a simulated exercise
A letter of recommendation and/or LinkedIn endorsement upon successful completion
Mentorship from our CTO on AI systems and data practices
Early-stage startup experience with a mission-driven team
Potential for paid roles as we grow
To apply, send your resume and a brief note about your interest to emily@skipittech.com. No cover letter required. We review applications on a rolling basis.
Skipit Tech, Inc. | skipit.tech | Lexington, MA (Remote)

Source: Skipit careers (LinkedIn)