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Male Fertility Data Annotation Specialist

Review sperm detection and motility labels in reconstructed microscopy videos for an AI-assisted semen analyzer. This contractor role pays $6-$14 per hour and requires at least two years of hands-on semen analysis experience.

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OpenTrain AI

Medical & Health

100% Remote Hourly · $6–$14/hr

$6–$14/hr

Compensation

Contract

Engagement

Remote

Location

Oct 19, 2026

Posted

Open worldwide

The Work

You will review and correct annotations used to train an AI-assisted semen analyzer. The data consists of short, grayscale microscopy video sequences with sperm marked by rotated bounding boxes and tracked across frames.

The images are computationally reconstructed rather than standard phase-contrast or brightfield views. A short paid onboarding session will explain how sperm appear in this imaging format.

  • Check machine-generated and human-drawn labels, add missed sperm, remove false positives such as debris, round cells, and artifacts, and correct box orientation.
  • Watch sequences of about two seconds and confirm or correct motility classes using WHO 6th-edition categories: progressive, non-progressive, and immotile.
  • Count sperm in defined regions when needed to compare annotation results with model output.
  • Write short notes about ambiguous cells, image-quality problems, and unclear parts of the annotation guideline.
  • A possible later feasibility phase may involve reviewing sperm morphology, including head, midpiece, and tail, using Kruger and WHO strict criteria.

What It Pays And Takes

This is contractor work paid by the hour. The role requires careful visual review, consistent use of written guidelines, and clinical semen-analysis experience.

  • Pay: $6-$14 USD per hour.
  • Location: Open worldwide; your country of residence is required for data-protection review.
  • Language: Fluent written English for notes and questions about the guidelines.
  • Experience: At least two years of hands-on manual semen analysis in a clinical andrology, IVF, or reproductive-medicine laboratory.
  • Required technical experience: Concentration testing with a hemocytometer and motility grading under WHO 5th or 6th edition standards.
  • Required equipment: Reliable internet and a desktop or laptop with a screen of at least 1080p. Phone and tablet work is not supported.
  • Application materials: A CV and verifiable proof of laboratory experience, including employer, role, and dates.
  • Before access: You must sign an NDA, IP assignment, and data-protection agreement.
  • Preferred credentials and experience include an embryology or andrology credential, EQA or proficiency-testing participation, Kruger strict morphology experience, CASA systems experience, or prior use of annotation tools such as Label Studio, Encord, CVAT, or V7.
  • You must provide your laboratory experience, years, approximate work volume, WHO edition used, credentials, EQA or proficiency participation, weekly availability, time zone, and country of residence.

How It Works

All work takes place inside the annotation platform. You may not download, screenshot, or copy project images, use AI tools or third parties on project data, or try to identify any participant. The data is de-identified research data.

Before production work, you complete a paid calibration task lasting about two to three hours. It is scored against expert reference labels, and agreement with those references is reviewed regularly afterward. You also receive brief guideline training and confirm it in a signed training record.

  • Apply on OpenTrain.
  • The employer reviews applications there.

About AI Training Work

AI training work is the human review behind systems that learn from labeled examples. In this role, your clinical experience helps create accurate examples for a model that analyzes semen microscopy data.

Requirements

  • Experience: Intermediate level
  • Languages: English

How to apply

  1. Apply here on OpenTrain. You create a free account and add your resume.
  2. Complete your application for this role on OpenTrain.

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