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Bioinformatic Engineer Jobs in Arkansas (NOW HIRING)

Ohio State University

$46K - $63K/yr

A doctoral degree, such as PhD, MD, MD/PhD, or equivalent, in Biomedical Engineering, Neurobiology ... bioinformatics/omics analysis including scRNA-seq or ATAC-seq, and AI-assisted data analysis.

Uasys

Research Coordinator

Little Rock, AR · On-site

... programming is highly desirable. The Research Coordinator will work closely with the Principal ... Doctoral degree (PhD) in Epidemiology, Public Health, Biostatistics, Bioinformatics, Molecular ...

Miamioh

Assistant/Associate Professor - CPB

Hughes, AR

Candidates with expertise in all areas of biomedical engineering are welcome, but preference will ... the Center for Bioinformatics and Functional Genomics, the Miami University Instrumentation ...

Posted today

Uasys

IFSC Graduate Assistant

Little Rock, AR

MS or PhD in Bioinformatics; * MS in Information Science or PhD in CIS Information Science; * MS in ... MS in Engineering or PhD in Engineering Science and Systems. Preferred Qualifications - Special ...

All JobsBioinformatic Engineer JobsBioinformatic Engineer Jobs in Arkansas

Bioinformatic Engineer information

See Arkansas salary details

$27.3K

$73.7K

$117.4K

How much do bioinformatic engineer jobs pay per year?

As of Aug 16, 2026, the average yearly pay for bioinformatic engineer in Arkansas is $73,746.00, according to ZipRecruiter salary data. Most workers in this role earn between $55,000.00 and $90,100.00 per year, depending on experience, location, and employer.

What is the difference between Bioinformatic Engineer vs Bioinformatics Analyst?

AspectBioinformatic EngineerBioinformatics Analyst
Required CredentialsBachelor's or Master's in Bioinformatics, Computer Science, or related fieldsBachelor's or Master's in Bioinformatics, Biology, or related fields
Work EnvironmentResearch labs, biotech companies, healthcare institutionsResearch institutions, healthcare, pharmaceutical companies
Employer & Industry UsageDevelops tools, pipelines, and software for biological data analysisAnalyzes biological data, interprets results, and reports findings

While both roles involve biological data, Bioinformatic Engineers focus on developing computational tools and pipelines, whereas Bioinformatics Analysts primarily interpret data and generate insights. Both positions require similar educational backgrounds and are vital in research and healthcare settings, but their core responsibilities differ in development versus analysis.

What are common challenges faced by bioinformatic engineers when working with large-scale genomic data?

Bioinformatic Engineers often encounter challenges related to managing and processing vast amounts of genomic data, which can require significant computational resources and efficient data handling strategies. Ensuring data integrity, reproducibility of analyses, and effective collaboration with multidisciplinary teams of biologists, statisticians, and software developers are also key aspects of the role. Staying updated with the latest bioinformatics tools and pipelines is essential, as the field evolves rapidly. Overcoming these challenges requires strong problem-solving skills, attention to detail, and the ability to communicate complex findings to non-technical stakeholders.

What skills and qualifications are needed to thrive as a bioinformatic engineer?

To thrive as a Bioinformatic Engineer, you need a strong background in computational biology, programming (such as Python or R), statistics, and a relevant degree in bioinformatics, computer science, or a related field. Familiarity with bioinformatics tools (like BLAST, GATK, or Bioconductor), data analysis platforms, and experience with databases such as SQL are typically required. Strong problem-solving abilities, attention to detail, and effective communication skills help you interpret complex data and collaborate with multidisciplinary teams. These skills are essential for accurately analyzing biological data, deriving meaningful insights, and facilitating research or clinical advancements.

What is a bioinformatic engineer?

Bioinformatic Engineers are professionals who develop and apply computational tools and techniques to analyze biological data, such as DNA sequences, protein structures, and genetic information. They combine expertise in computer science, biology, and mathematics to process large datasets generated by modern biological experiments. Their work supports research in areas like genomics, drug discovery, and personalized medicine by helping scientists make sense of complex biological data.

What are popular job titles related to Bioinformatic Engineer jobs in Arkansas?

For Bioinformatic Engineer jobs in Arkansas, the most frequently searched job titles are:

What job categories do people searching Bioinformatic Engineer jobs in Arkansas look for?

The top searched job categories for Bioinformatic Engineer jobs in Arkansas are:

Bioinformatic Engineer jobs near you
Infographic showing various Bioinformatic Engineer job openings in Arkansas as of August 2026, with employment types broken down into 91% Full Time, 4% Part Time, and 5% Contract. Highlights an 86% Physical, 5% Hybrid, and 9% Remote job distribution, with an average salary of $73,746 per year, or $35.5 per hour.

Bioinformatics Software Engineer

micro1 AI

Little Rock, AR • Remote

$80 - $110/hr

Part-time

Posted 14 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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