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Data Annotation Ai Trainer Jobs in Michigan (NOW HIRING)

Siemens AG

Data Governance AI Security Engineer

Milford, MI · On-site

$110 - $198/hr

Tracking end-to-end data provenance (where training/RAG data originated, who modified it, and how it was sanitized). * Ensuring compliance with corporate guidelines on data and AI usage and AI ...

Posted today

Kiongroup

Senior AI Data Engineer

Grand Rapids, MI · On-site

$121K - $151K/yr

... training. The base pay range for this role is estimated to be $121,200.00 - $151,500.00 at the time ... Evolve the Enterprise Data Platform into an AI-native platform by enabling intelligent discovery ...

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All JobsData Annotation Ai Trainer JobsData Annotation Ai Trainer Jobs in Michigan

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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Data Annotation Ai Trainer jobs near you
Infographic showing various Data Annotation Ai Trainer job openings in Michigan as of August 2026, with employment types broken down into 1% As Needed, 80% Full Time, 13% Part Time, 2% Temporary, 3% Contract, and 1% Nights. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution.

AI Training Specialist - Cheminformatics

micro1 AI

Warren, MI • Remote

$80 - $110/hr

Part-time

Posted 20 days ago

Job description

Role Title: Computational Biology & Cheminformatics Expert


Role Type: Contractor


Location: Remote


micro1 is engaging Computational Biology & Cheminformatics Experts to contribute their expertise to a customer’s computational drug discovery 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 and interpret small-molecule and drug discovery datasets using advanced computational biology, bioinformatics, and cheminformatics methods.
  2. Curate, annotate, and validate chemical and biological datasets (e.g., ChEMBL, PubChem, DrugBank) to support AI-driven discovery platforms.
  3. Evaluate compound-target interactions, ADMET properties, and lead optimization strategies by integrating chemical, biological, and clinical data sources.
  4. Provide expert insights on structure-activity and structure-property relationships (SAR/SPR), medicinal chemistry approaches, and experimental design considerations.
  5. Build and implement code-based benchmark tasks (e.g., terminal/CLI-based environments) that reflect realistic computational drug discovery scenarios.
  6. Develop reproducible environments (e.g., using Docker) and automated testing pipelines to ensure task correctness and solvability.
  7. Assess and review AI-generated outputs for scientific rigor, accuracy, and practical relevance, delivering detailed written feedback and recommendations.


Preferred Qualifications

  1. Advanced expertise in Computational Biology, Cheminformatics, Medicinal Chemistry, Biochemistry, or related fields; advanced degree (PhD, MSc, PharmD) highly valued but not strictly required.
  2. Strong coding proficiency in Python (beyond analysis scripts), with hands-on experience building tools, pipelines, or testable code; familiarity with Git, GitHub, and Docker.
  3. Extensive experience with cheminformatics toolkits and platforms such as RDKit, KNIME, Schrödinger, OpenEye, or MOE.
  4. Proven track record in small-molecule drug discovery, SAR/QSAR evaluation, ADMET prediction, or virtual screening workflows.
  5. Comfort working with public chemical and bioactivity databases and integrating diverse datasets for scientific analysis.
  6. Demonstrated ability to clearly communicate complex chemical and biological concepts in written feedback and reports.
  7. Experience participating in multidisciplinary and/or remote projects; familiarity with AI-assisted coding tools is a plus.

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