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How much do internship remote ai training jobs pay per year?

As of Aug 23, 2026, the average yearly pay for internship remote ai training in the United States is $32,342.00, according to ZipRecruiter salary data. Most workers in this role earn between $30,000.00 and $33,500.00 per year, depending on experience, location, and employer.

What is a remote AI training internship?

A remote AI training internship is a temporary position where interns work online to help train artificial intelligence systems. This may involve tasks like labeling data, evaluating AI outputs, or helping improve machine learning models. Interns collaborate with teams using digital tools and gain hands-on experience in the AI field, all while working from home or another remote location. These internships are valuable for students or recent graduates looking to build skills in artificial intelligence and related technologies.

What are the key skills and qualifications needed to thrive in a remote AI training internship?

To thrive as an Internship Remote AI Training participant, you generally need a background in computer science, statistics, or a related field, along with foundational knowledge in machine learning concepts. Familiarity with programming languages like Python, machine learning libraries such as TensorFlow or PyTorch, and basic data labeling tools is often required. Strong attention to detail, effective communication, and self-motivation are valuable soft skills for excelling in remote, collaborative, and iterative environments. These skills ensure accurate data handling, productive teamwork, and continuous learning, all of which are crucial for successful AI model development.

What are some common challenges faced by interns in remote AI training roles, and how can they be overcome?

Interns in remote AI training roles often encounter challenges such as limited real-time mentorship, difficulty staying motivated, and navigating complex technical tasks independently. To overcome these, it's important to actively communicate with your team through scheduled check-ins, participate in virtual collaborative sessions, and utilize project management tools to stay organized. Seeking feedback regularly and leveraging online resources or company-provided documentation can also help build confidence and skill. By taking initiative and engaging proactively, interns can make the most of their remote experience and contribute effectively.

What is the difference between Internship Remote Ai Training vs Data Analyst Intern?

AspectInternship Remote Ai TrainingData Analyst Intern
Required CredentialsBasic understanding of AI, programming skills (Python, SQL)Statistics, data analysis, Excel, SQL
Work EnvironmentRemote, collaborative online platformsRemote or on-site, data-focused teams
Industry UsageAI development, machine learning projectsBusiness intelligence, data reporting
Search & Comparison IntentLearning AI training, entry-level AI rolesData analysis, internship opportunities

Internship Remote Ai Training focuses on developing skills in artificial intelligence and machine learning, often requiring programming knowledge. Data Analyst Internships emphasize analyzing data sets, creating reports, and using statistical tools. Both are entry-level roles with remote options, but they serve different industry functions and skill sets.

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Internship Remote Ai Training jobs near you
Infographic showing various Internship Remote Ai Training job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 16% Part Time, and 1% Contract. Highlights an 89% Physical, 1% Hybrid, and 10% Remote job distribution, with an average salary of $32,342 per year, or $15.5 per hour.

AI Training Specialist - Cheminformatics

micro1 AI

Huntsville, AL • 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.

Apply