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Live In Ai Validation Jobs in Arizona (NOW HIRING)

micro1 AI

AI Training Specialist - Physics

Mesa, AZ · On-site +1

$80 - $150/hr

No prior experience in AI is required -- your domain knowledge is what matters. Scope of Work ... validation. * Demonstrated skill in reviewing the work of others--through peer review, supervision ...

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Live In Ai Validation information

What are the key skills and qualifications needed to thrive as a live in AI validation specialist?

To thrive as a Live In AI Validation Specialist, you need a solid background in computer science, data analysis, and machine learning concepts, often supported by a relevant degree or equivalent experience. Familiarity with tools such as Python, TensorFlow, and data annotation platforms, as well as an understanding of AI validation protocols, is typically required. Attention to detail, critical thinking, and strong communication skills are essential soft skills for ensuring accurate validation and effective collaboration with development teams. These competencies are crucial for maintaining AI system quality, reliability, and alignment with real-world requirements.

What are some common challenges faced by professionals in live in AI validation roles, and how can they be addressed?

Professionals in Live-In AI Validation often encounter challenges such as managing large-scale data collection in real-time environments and ensuring the accuracy of AI outputs in dynamic, real-world settings. Collaboration with data scientists, engineers, and end users is key to troubleshooting unexpected behaviors and improving system reliability. Strong communication and adaptability help team members quickly respond to new scenarios or hardware changes during validation. Staying up-to-date with the latest AI validation tools and continuous feedback loops can also significantly enhance the effectiveness of the validation process.

What is a live in AI validation job?

Live In AI Validation jobs involve working closely with artificial intelligence systems to test, review, and improve their performance in real-world settings. Professionals in this role typically monitor the outputs of AI algorithms, validate data quality, and provide feedback to ensure the AI operates accurately and ethically. These jobs may require living on-site or being embedded in environments where the AI is deployed, such as smart homes, research labs, or automated facilities. The goal is to bridge the gap between AI development and real-world application, ensuring the technology is reliable and effective.

What is the difference between Live In Ai Validation vs Live In Data Entry?

AspectLive In Ai ValidationLive In Data Entry
Required CredentialsBasic computer skills, attention to detailBasic computer skills, attention to detail
Work EnvironmentRemote, client-facing, flexible hoursRemote, client-facing, flexible hours
Industry UsageAI, tech, data servicesVarious industries, administrative tasks
Common Search IntentAI validation, data verification jobsData entry, administrative jobs

Live In Ai Validation involves verifying and validating AI-generated data to ensure accuracy, often requiring critical thinking and familiarity with AI tools. In contrast, Live In Data Entry focuses on inputting data into systems, emphasizing speed and accuracy. Both roles are remote and require similar skills but serve different functions within the data management industry.

What are the most commonly searched types of Ai Validation jobs in Arizona?

The most popular types of Ai Validation jobs in Arizona are:

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For Live In Ai Validation jobs in Arizona, the most frequently searched job titles are:

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Live In Ai Validation jobs near you

AI Training Specialist - Cheminformatics

micro1 AI

Chandler, AZ • Remote

$80 - $110/hr

Part-time

Posted 13 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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