Sleep Data Annotator for AI Training & Analysis

Braintrust; United States; On-site; USD 50,000 - 66,000; Part time

Job summary

Braintrust is seeking sleep-science and behavioral-sleep professionals to support an AI training and evaluation initiative. You will review and annotate sleep patterns, device data, logs, and self-reports for scientific accuracy, safety, and consistency.

You will annotate sleep stages, durations, onset/wake times, and disturbances from various data sources, and compare self-reports with device data. Strong attention to detail and time-series comfort are essential.

Qualifications

• At least 3 years of relevant experience in sleep science, physiology, psychology, sleep technology, or a related health field.
• Familiarity with actigraphy, PSG basics, consumer wearables, and sleep-tracking technologies.
• Strong attention to detail, pattern recognition, and comfort with time-series data.
• Comfort using annotation platforms or structured review tools.
• Ability to commit for 8 weeks, work 8 hours per day, and overlap at least 4 hours with Pacific Time.

Responsibilities

• Annotate sleep stages, duration, onset and wake times, and disturbances from sensor data, logs, or diaries.
• Classify sleep-quality indicators and patterns associated with concerns such as insomnia or irregular schedules.
• Compare self-reported sleep information with device or sensor data.
• Flag anomalies, missing data, and device or measurement errors.
• Review AI-generated sleep-health content for scientific accuracy, safety, and relevance.
• Apply and refine standardized annotation guidance across large datasets.

Skills

Sleep science

Time-series data

Attention to detail

Actigraphy basics

Education

Health field background

Tools

Annotation platforms

Job description

Braintrust is seeking sleep-science and behavioral-sleep professionals to support an AI training and evaluation initiative. You will review and annotate sleep patterns, device data, logs, and self-reports for scientific accuracy, safety, and consistency.

You will annotate sleep stages, durations, onset/wake times, and disturbances from various data sources, and compare self-reports with device data. Strong attention to detail and time-series comfort are essential.