Remote Data Annotation Specialist

Job description A well-known AI firm that is at the forefront of innovative data development techniques is looking for Data Annotation Specialists with a background in medicine. To train machine learning models, you will label, classify, and assess data. This covers tasks like labeling text, creating and assessing multiple-choice questions, and marking objects in photos to help AI models that rely on accurately labeled data.

We are looking for individuals who can do activities that take 10–40 minutes while maintaining a high degree of attention to detail. For those who want to help progress AI, this opportunity may lead to future involvement in other initiatives.

Location

United States (Remote)

Schedule

Flexible: 10-40 hours per week (US-based)

Pay

25.00 USD Per Hour

Duties and Responsibilities

• Annotation of multimodal data: Precisely assign labels and tags to data, such as images and text, based on the given instructions.
• Generate multiple-choice questions that are clear and of high quality for educational or AI training needs.
• Quality assurance: Confirm accuracy and uniformity in your annotations by reviewing and verifying them across datasets.
• Data processing involves managing extensive amounts of data, completing tasks within project timelines, and paying close attention to detail.

Requirements

• Bachelor’s Degree or advanced degree in a field related to Medicine (such as Pre-Med, Medicine, Osteopathic Medicine, Nursing, Pharmacy, Dental Surgery, Dental Medicine)
• Precision is paramount when it comes to paying attention to detail, as inaccuracies in labeling can directly affect how well machine learning models perform.
• Proficiency in English: Exceptional written and oral communication abilities, particularly in creating content and annotating text data.
• Self-drive: Skill to independently complete tasks and efficiently use time while working remotely.
• Preference is given to those with experience or familiarity in handling and processing different types of data when it comes to data literacy.
• Technical proficiency: Being at ease with fundamental software tools, spreadsheets, or annotation platforms can be advantageous.