AI Training Health Informatics Analyst β€” Remote

AI Training Health Informatics Analyst β€” Remote

Full-Time 60000 - 80000 Β£ / year (est.) No working from home possible
Alignerr

At a Glance

  • Tasks: Evaluate AI training on healthcare data and analyse AI-generated health content.
  • Company: Alignerr, a forward-thinking company in the health informatics space.
  • Benefits: Flexible hours, freelance perks, and the ability to work remotely.
  • Other info: Enjoy working independently in a dynamic, remote environment.
  • Why this job: Make a real impact on healthcare by improving AI model accuracy.
  • Qualifications: Experience in health informatics and strong analytical skills.

The predicted salary is between 60000 - 80000 Β£ per year.

Alignerr is seeking a Health Informatics Analyst to help evaluate AI training on healthcare data.

This remote role offers flexible hours and freelance perks, with 10–40 hours per week.

You will analyze AI-generated health content, review how systems handle clinical terminology, and provide expert feedback to improve model accuracy.

You will identify gaps and communicate findings to AI teams, enabling safer, more reliable health information.

Work asynchronously and independently from home.

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AI Training Health Informatics Analyst β€” Remote employer: Alignerr

Alignerr is an exceptional employer that champions innovation in healthcare through AI, offering a dynamic remote work environment that fosters collaboration and creativity. Employees benefit from a culture of continuous learning and professional development, with ample opportunities to grow within the organisation while contributing to groundbreaking projects that have a real impact on clinical research. Join us to be part of a forward-thinking team dedicated to transforming healthcare for the better.

Alignerr

Contact Details:

Alignerr Recruitment Team

We think you need these skills to ace AI Training Health Informatics Analyst β€” Remote

Health Informatics
AI Training Evaluation
Data Analysis
Clinical Terminology Review
Expert Feedback Provision
Model Accuracy Improvement
Gap Identification