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How much do evening remote ai training jobs pay per hour?

As of Aug 15, 2026, the average hourly pay for evening remote ai training in the United States is $40.50, according to ZipRecruiter salary data. Most workers in this role earn between $28.61 and $47.84 per hour, depending on experience, location, and employer.

How does an evening remote AI training role typically collaborate with other team members?

In an Evening Remote AI Training position, collaboration often happens asynchronously through shared documentation, project management tools, and scheduled check-ins with team members who may work during standard hours. Communication is usually facilitated via messaging platforms and regular virtual meetings to ensure alignment and progress. While direct real-time interaction may be limited due to the evening schedule, clear documentation and proactive updates are crucial for seamless teamwork. This structure allows for flexibility but requires strong self-motivation and organization to stay connected and contribute effectively to the team's goals.

What skills and qualifications are needed to thrive as an evening remote AI trainer?

To thrive as an Evening Remote AI Trainer, you need a solid understanding of artificial intelligence concepts, data annotation techniques, and relevant educational or technical qualifications. Familiarity with AI training platforms, data labeling tools, and collaboration software is typically required. Strong communication, attention to detail, and time management skills help you effectively convey feedback and meet project deadlines. These abilities ensure high-quality AI model training and efficient remote teamwork, which are critical for successful project outcomes.

What is an evening remote AI training job?

Evening Remote AI Training jobs involve working from home during evening hours to help train artificial intelligence systems. These roles typically require you to complete tasks such as labeling data, annotating images or text, or evaluating AI-generated content. Evening shifts offer flexibility for those who have daytime commitments or prefer working later in the day. These jobs can range from entry-level to more advanced roles, depending on the complexity of the AI projects. Most positions require basic computer skills, attention to detail, and sometimes specialized knowledge, depending on the industry.

What is the difference between Evening Remote Ai Training vs Data Labeler?

AspectEvening Remote Ai TrainingData Labeler
CredentialsBasic computer skills, sometimes certifications in AI or data processingNone typically required, training provided
Work EnvironmentRemote, flexible hours, often evening shiftsRemote or on-site, flexible or part-time hours
Industry UsageAI development, machine learning projectsData preparation for AI and machine learning
Search & Comparison IntentUnderstanding roles in AI training, flexible AI-related jobsEntry-level data annotation, data labeling jobs

Evening Remote Ai Training involves learning and training AI models remotely during evening hours, often requiring some technical knowledge. Data Labeler focuses on annotating data for AI systems, usually with minimal prior experience. Both roles are remote and support AI development, but Ai Training emphasizes skill development, while Data Labeling is more task-oriented.

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Evening Remote Ai Training jobs near you
Infographic showing various Evening Remote Ai Training job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 80% Full Time, 17% Part Time, and 2% Contract. Highlights an 95% Physical, 1% Hybrid, and 4% Remote job distribution, with an average salary of $84,240 per year, or $40.5 per hour.

AI Training Specialist - Cheminformatics

micro1 AI

Oklahoma City, OK • Remote

$80 - $110/hr

Part-time

Posted 12 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