Data Annotation Reviewer

About the role

This position reviews training data for a large-scale egocentric video dataset within the AI and robotics field, where your scrutiny directly shapes the quality of client deliverables. You will own the evaluation of language annotations, ensuring clarity, accuracy, and strict adherence to detailed labeling guidelines across every batch. The role requires you to check action segmentation and timing so that video segments form correctly timed, non-overlapping actions that align with project standards. You will flag compliance issues to protect privacy and uphold collection quality and content standards, exercising judgment on whether batches should be approved for delivery or rejected. Monitoring throughput will be part of your responsibility to ensure delivery targets are met without compromising the accuracy that clients demand. You will review calibration sets to maintain alignment with other reviewers across shared assessments, preserving consistency in evaluation standards. This role operates within a remote, target-driven environment that requires comfort with regular calibration checks and a high degree of self-direction.

Key facts

What you'll do

Language annotations are evaluated for clarity, accuracy, and consistency with detailed labeling guidelines to ensure high-quality training data.

Action segmentation and timing are checked so video segments form correctly timed, non-overlapping actions that reflect true user intent.

Compliance issues are flagged to protect privacy and meet collection quality and content standards, reducing legal and ethical risk.

Judgment on flagged segments determines whether batches are approved for delivery or rejected, directly impacting project timelines.

Throughput is monitored to ensure delivery targets are met without compromising accuracy, balancing speed with meticulous review practices.

Calibration sets are reviewed to maintain alignment with other reviewers across shared assessments, ensuring consistency in evaluation methodology.

Video playback tools are used to perform detailed reviews, requiring precise control over timing, playback speed, and annotation verification.

Clear written communication is essential for labeling consistency, enabling other team members to understand and replicate your decisions.

Quality assurance processes rely on calibrated guidelines and repeated audits to maintain high standards across large volumes of data.

Remote collaboration is conducted through digital tools, requiring strong asynchronous communication to resolve annotation discrepancies effectively.

Edge-case segments are handled with reliable judgment, ensuring that ambiguous situations are treated according to established protocols.

Continuous focus on detail supports sustained, cognitively demanding review sessions, where accuracy must be maintained over long work periods.

You will contribute to building a robust dataset that supports advanced computer vision tasks such as hand tracking and action segmentation.

Feedback on your reviews is used to refine guidelines and improve team-wide consistency, making your role integral to long-term data quality.

Requirements

One year or more of experience as a Data Annotation Reviewer is required to demonstrate familiarity with video-based annotation workflows.

Written and reading English comprehension must be excellent for applying nuanced guidelines and resolving complex annotation disputes.

Detail-oriented focus supports sustained, cognitively demanding review sessions where lapses could affect dataset integrity.

Consistent judgment handles ambiguous and edge-case segments reliably, ensuring decisions align with project standards.

Reliable high-speed internet and a capable device enable smooth video playback, which is essential for accurate review work.

Comfort in a remote, target-driven environment with regular calibration checks is necessary to maintain performance and alignment.

A stated degree requirement must be met, reflecting the importance of formal education in this data-centric role.

Equal employment practices apply to all employment terms and candidate interactions, ensuring fairness in the selection process.

Nice to have

Only items specified as preferred in the source are included, and no additional preferences are added.

Practical notes

This role operates remotely from Manila under target-driven quality standards, requiring reliable connectivity and self-management.

Equal employment practices apply to all employment terms and candidate interactions.

Typical interview steps involve data interviews that may include a SQL or coding exercise, a statistics question, and a case study where you might design a metric, interpret an experiment, or build a small model.

Candidates may be asked to review past projects, discuss business impact, and explain how they communicated uncertainty during previous assignments.

Bringing a clean write-up of a past analysis to the interview is well received and can demonstrate your attention to detail.

Work in data annotation often involves precision review of video and motion information, making experience with similar content valuable.

Hand tracking and action segmentation are common concepts in computer vision projects that you will encounter in this role.

Quality assurance processes rely on calibrated guidelines and repeated audits to ensure consistency across large datasets.

Remote teams use video playback tools for review, making technical comfort with these systems a practical advantage.

Clear written communication is essential for labeling consistency and effective collaboration with other reviewers.

Questions to ask

Useful questions for the interview include what a typical week looks like, how work is assigned, what tools the team uses, and how feedback is incorporated into future reviews.

Asking how the role has changed recently and what the team wishes it had known when joining provides insight into evolving responsibilities.

Questions about the manager's priorities are especially valued and can help you understand expectations and success criteria.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead, offering multiple paths for advancement.

Many professionals specialize in machine learning, analytics, or infrastructure, allowing deeper expertise in specific domains.

Cross-functional work with product and engineering teams becomes more important at senior levels, increasing your impact on organizational goals.

The field changes quickly, so continuous learning is part of the job, requiring adaptation to new tools, methods, and standards.

Professionals who can translate numbers into decisions tend to advance fastest, as they bridge analysis and business action effectively.

About the location

The Philippines' capital is dense, loud, and affordable, offering a dynamic environment for remote work.

BGC (Bonifacio Global City) and Makati concentrate the coworking scene, providing professional spaces for focused work.

Internet speeds have improved sharply since 2023, supporting reliable video review and real-time collaboration.

Monthly costs around $1,100, making it a viable location for remote professionals balancing cost of living and lifestyle.

Traffic is severe, so picking a neighborhood and staying put is recommended to reduce commute stress and save time.