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Executive Remote Computational Chemistry information

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$8.3K

$11.5K

$15.1K

How much do executive remote computational chemistry jobs pay per month?

As of Sep 6, 2026, the average monthly pay for executive remote computational chemistry in the United States is $11,473.00, according to ZipRecruiter salary data. Most workers in this role earn between $8,541.67 and $14,416.67 per month, depending on experience, location, and employer.

What is the difference between Executive Remote Computational Chemistry vs Computational Chemist?

AspectExecutive Remote Computational ChemistryComputational Chemist
CredentialsAdvanced degrees (PhD preferred), leadership experienceMaster's or PhD in Chemistry, Chemistry-related fields
Work EnvironmentRemote, strategic, leadership-focusedRemote or on-site, research and analysis-focused
Industry UsageUsed in leadership, strategic planning, and client interactionsUsed in research, development, and scientific analysis

Executive Remote Computational Chemistry roles focus on strategic leadership, client management, and high-level decision-making, often requiring advanced degrees and leadership experience. In contrast, Computational Chemists primarily conduct research, modeling, and analysis, with a focus on scientific expertise. Both roles may be remote but serve different functions within the industry.

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Executive Remote Computational Chemistry jobs near you
Infographic showing various Executive Remote Computational Chemistry job openings in the United States as of August 2026, with employment types broken down into 1% Internship, 89% Full Time, 6% Part Time, and 4% Contract. Highlights an 84% Physical, 4% Hybrid, and 12% Remote job distribution, with an average salary of $137,676 per year, or $66.2 per hour.

Medical Microbiology Consultant - Remote

micro1 AI

Irving, TX • Remote

$70 - $90/hr

Part-time

Re-posted 9 days ago

Job description

Role Title: Microbiologist


Role Type: Contractor


Location: Remote


micro1 is engaging Microbiologists to contribute their scientific expertise to a unique customer 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.


Key Responsibilities:

  1. Investigate and analyze the development, morphology, and behavior of microscopic organisms including bacteria, fungi, and algae.
  2. Contribute to the study of the relationship between microorganisms and disease, supporting projects involving medical microbiology.
  3. Assess the impact of antibiotics and other agents on microbial populations, providing insights for AI model accuracy.
  4. Document experimental findings and processes with a focus on clarity for AI training data.
  5. Collaborate with interdisciplinary teams to ensure scientific rigor and data integrity in AI development.
  6. Provide written and verbal expertise on microbiological phenomena and their relevance to real-world and computational contexts.
  7. Utilize rubrics and established evaluation criteria to assess data quality and support AI training workflows.


Required Skills and Qualifications:

  1. Bachelor’s degree or higher in Biology, Microbiology, Chemistry, or a related field.
  2. Extensive knowledge of bacterial, fungal, and algal systems.
  3. Demonstrated expertise in investigating microbial structure and physiology.
  4. Strong written and verbal communication skills for technical and interdisciplinary collaboration.
  5. Ability to document processes and findings clearly for integration into AI systems.
  6. Comfort working independently in a fully remote, digital-first environment.
  7. Attention to detail and commitment to scientific accuracy.


Preferred Qualifications:

  1. Prior experience developing or applying rubrics in scientific or educational contexts.
  2. Experience with AI, machine learning, or annotation projects related to biology or microbiology.
  3. Advanced degree (Master’s or PhD) in a relevant field.

Apply