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Model Validation Remote Jobs in Austin, TX (NOW HIRING)

micro1 AI

Cheminformatics Specialist - Remote

Austin, TX · Remote

$80 - $110/hr

Your work will shape how models learn, reason, and perform through high-quality, real-world input ... Curate, annotate, and validate chemical and biological datasets (e.g., ChEMBL, PubChem, DrugBank ...

YO AI Labs

AI Data Science Expert - Remote

Austin, TX · Remote

$100 - $200/hr

Develop, optimize, and evaluate prompts to improve AI model performance. * Conduct rubric-based ... Perform independent research and fact-checking to validate technical information. * Annotate data ...

New

YO AI Labs

AI Data Science Expert - Remote

Austin, TX · Remote

$100 - $200/hr

Develop, optimize, and evaluate prompts to improve AI model performance. * Conduct rubric-based ... Perform independent research and fact-checking to validate technical information. * Annotate data ...

New

YO AI Labs

AI Data Scientist Expert - Remote

Austin, TX · Remote

$100 - $200/hr

Develop, optimize, and evaluate prompts to improve AI model performance. * Conduct rubric-based ... Perform independent research and fact-checking to validate technical information. * Annotate data ...

New

YO AI Labs

AI Finance Expert - Remote

Austin, TX · Remote

$100 - $200/hr

Remote Job Overview We are seeking experienced AI Finance Domain Experts to contribute their ... checking to validate financial information. * Provide detailed feedback to improve AI model ...

New

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All JobsModel Validation JobsModel Validation Remote JobsModel Validation Remote Jobs in Austin, TX

Model Validation Remote information

See Austin, TX salary details

$22

$51

$77

How much do model validation remote jobs pay per hour?

As of Aug 13, 2026, the average hourly pay for model validation remote in Austin, TX is $51.54, according to ZipRecruiter salary data. Most workers in this role earn between $39.09 and $62.64 per hour, depending on experience, location, and employer.

What is the difference between Model Validation Remote vs Model Validation on-site?

AspectModel Validation RemoteModel Validation on-site
Work EnvironmentRemote, home-basedOn-site, office or client location
Required CredentialsSimilar certifications, e.g., CFA, FRM, or relatedSame as remote, often with additional in-person requirements
Industry UsageFinancial institutions, banks, asset managersSame industries, with in-person collaboration
Work FlexibilityHigh, flexible hours and locationLess flexible, fixed hours and location

Both remote and on-site model validation roles require similar credentials and industry knowledge. The main difference lies in the work environment and flexibility, with remote positions offering greater convenience and location independence, while on-site roles facilitate direct collaboration and immediate access to resources.

What are the key skills and qualifications needed to thrive as a model validation remote?

To thrive as a Model Validation Remote, you need a strong background in quantitative disciplines such as mathematics, statistics, or finance, typically supported by a relevant degree. Proficiency with statistical software (like SAS, R, or Python), model risk management frameworks, and familiarity with regulatory guidelines (such as SR 11-7) are commonly required. Analytical thinking, attention to detail, and strong written communication are crucial soft skills in this role. These skills ensure accurate model assessments, regulatory compliance, and effective communication of complex findings to stakeholders.

What is model validation in a remote job context?

Model validation, especially in a remote setting, involves evaluating and verifying the accuracy, performance, and reliability of statistical or machine learning models from a location outside of a traditional office. Professionals in this role typically assess whether models meet regulatory requirements, function as intended, and are free from biases or errors. Remote model validators use various tools and techniques to conduct tests, write reports, and communicate findings with stakeholders via digital platforms. This work is essential in sectors like finance, insurance, and tech, where robust models drive critical decisions. Successful remote model validation requires strong analytical skills, clear communication, and proficiency with data analysis tools.

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

Remote model validation professionals often encounter challenges such as maintaining clear communication with model developers and stakeholders, accessing secure data environments, and staying updated with evolving regulatory standards. To address these, it's important to leverage robust collaboration tools, schedule regular check-ins with cross-functional teams, and participate in ongoing training or knowledge-sharing sessions. Establishing clear documentation protocols and ensuring secure remote access to necessary data can also help maintain productivity and compliance.

What job categories do people searching Model Validation Remote jobs in Austin, TX look for?

The top searched job categories for Model Validation Remote jobs in Austin, TX are:

What cities near Austin, TX are hiring for Model Validation Remote jobs?

Cities near Austin, TX with the most Model Validation Remote job openings:

Model Validation Remote jobs near you
Infographic showing various Model Validation Remote job openings in Austin, TX as of August 2026, with employment types broken down into 3% Internship, 77% Full Time, 9% Part Time, and 11% Contract. Highlights an 100% Remote job distribution, with an average salary of $107,202 per year, or $51.5 per hour.

Cheminformatics Specialist - Remote

micro1 AI

Georgetown, TX • 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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