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Data Annotation Ai Trainer Jobs in Utah (NOW HIRING)

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Data Annotation Ai Trainer information

What is a data annotation AI trainer?

A Data Annotation AI Trainer is responsible for labeling and annotating data to help train machine learning models. This involves identifying objects, tagging text, or categorizing images to improve AI accuracy. The role requires attention to detail and an understanding of guidelines to ensure high-quality labeled data. AI trainers work closely with data scientists and engineers to refine model performance through precise annotations.

What does a data annotation AI trainer do?

As a Data Annotation Ai Trainer, your typical day involves reviewing and labeling large datasets, providing feedback to annotation teams, and ensuring that data quality meets project standards. You'll often collaborate with data scientists, machine learning engineers, and project managers to clarify guidelines and resolve ambiguities. Periodically, you may help develop or refine documentation and training materials to improve annotation consistency. The role requires both independent work and open communication to maintain high accuracy and support AI development initiatives.

What are the key skills and qualifications needed to thrive as a data annotation AI trainer?

To thrive as a Data Annotation Ai Trainer, you need a keen attention to detail, basic data analysis skills, and familiarity with machine learning concepts, often supported by a relevant degree or coursework. Experience with annotation tools like Labelbox, Supervisely, or similar platforms, along with knowledge of data privacy standards, is commonly required. Strong communication, problem-solving ability, and patience help you work effectively in teams and ensure data quality. These skills are essential because they directly influence the accuracy and effectiveness of AI models trained using annotated data.

How much do data annotation AI trainers make?

Data annotation AI trainers typically earn between $15 and $30 per hour, depending on experience, location, and the complexity of the annotation tasks. Salaries can range from entry-level rates to higher wages for specialized skills or advanced tools proficiency.

Is data annotation AI trainer job legitimate?

Data annotation AI trainer jobs are legitimate roles involving labeling data to help train machine learning models. These positions often require attention to detail and familiarity with annotation tools, and they are commonly offered by tech companies and data labeling firms. However, job seekers should verify the employer's credibility to avoid scams.

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The top searched job categories for Data Annotation Ai Trainer jobs in Utah are:

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Cities in Utah with the most Data Annotation Ai Trainer job openings:

Data Annotation Ai Trainer jobs near you
Infographic showing various Data Annotation Ai Trainer job openings in Utah as of August 2026, with employment types broken down into 33% Full Time, and 67% Part Time. Highlights an 100% In-person job distribution.

AI Training Specialist - Life Sciences

micro1 AI

West Jordan, UT • Remote

$90 - $120/hr

Part-time

Posted 22 days ago

Job description

Role Title: Bioinformatics Scientist


Role Type: Contractor


Location: Remote


micro1 is engaging Bioinformatics Scientists to contribute their specialized expertise to a customer's innovative project. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters.


Scope of Work

  1. Analyze complex datasets related to medicinal chemistry using advanced bioinformatics methodologies.
  2. Provide detailed scientific input and content to support the development and training of AI models.
  3. Curate, annotate, and validate datasets relevant to drug discovery and molecular analysis.
  4. Evaluate and synthesize findings from biological, chemical, and clinical data sources.
  5. Offer subject matter expertise on experimental design and data interpretation within medicinal chemistry.
  6. Assess AI-generated outputs for scientific accuracy, relevance, and reliability.
  7. Deliver comprehensive written feedback and actionable recommendations for model improvement.


Preferred Qualifications

  1. Advanced degree (e.g., PhD or MSc) in Bioinformatics, Computational Biology, Medicinal Chemistry, or a related discipline.
  2. In-depth knowledge of medicinal chemistry concepts, including structure-activity relationships and drug design principles.
  3. Demonstrated experience in handling and interpreting large-scale omics or cheminformatics datasets.
  4. Familiarity with software tools, databases, and programming languages commonly used in bioinformatics (e.g., Python, R, RDKit, KNIME).
  5. Strong scientific communication skills, with the ability to clearly articulate complex ideas and technical concepts.
  6. Proven track record of contributing to research projects at the intersection of biology, chemistry, and data science.
  7. Experience collaborating in multidisciplinary or remote project environments is advantageous.

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