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Remote Junior Biologist Jobs (NOW HIRING)

Dudek

Mid-Senior Biologist

$68K - $110K/yr

Field / Remote Compensation: $68,000-$110,000 annually* Dudek's journey began in 1980 with a vision ... A Biologist III focuses on project leadership, community coordination, and mentoring junior staff ...

Dudek

Mid-Senior Biologist

$68K - $110K/yr

Field / Remote Compensation: $68,000-$110,000 annually* Dudek's journey began in 1980 with a vision ... A Biologist III focuses on project leadership, community coordination, and mentoring junior staff ...

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$37K

$89.4K

$138K

How much do remote junior biologist jobs pay per year?

As of Aug 21, 2026, the average yearly pay for remote junior biologist in the United States is $89,403.00, according to ZipRecruiter salary data. Most workers in this role earn between $59,500.00 and $121,000.00 per year, depending on experience, location, and employer.

What is a remote junior biologist?

Remote junior biologists are early-career biology professionals who work primarily from home or off-site locations rather than in a traditional laboratory or office. They typically support research projects, data analysis, field studies, or lab work virtually, often under the supervision of more senior biologists. Their tasks might include collecting and analyzing data, assisting with report writing, and participating in virtual meetings with research teams. Remote junior biologists can work in various fields such as ecology, environmental science, genetics, or conservation, depending on the employer's focus. This flexible role is becoming more common as technology enables collaboration and research from anywhere.

What are the key skills and qualifications needed to thrive as a remote junior biologist, and why are they important?

To thrive as a Remote Junior Biologist, you generally need a bachelor’s degree in biology or a related science, solid research skills, and experience with data analysis. Familiarity with laboratory information management systems (LIMS), statistical software like R or Python, and digital collaboration tools is typical. Strong attention to detail, self-motivation, and effective written communication are valuable soft skills for remote work. These abilities ensure you can conduct accurate research, collaborate with teams from a distance, and contribute meaningful scientific insights.

What are some typical challenges faced by remote junior biologists, and how can they overcome them?

Remote Junior Biologists often face challenges related to limited direct supervision, managing independent research, and staying connected with their teams. To overcome these, it's important to establish regular communication with colleagues via video calls or collaboration platforms, seek feedback proactively, and maintain organized records of research activities. Participating in virtual team meetings and online professional development opportunities can also help build relationships and foster growth, even when working remotely.
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Remote Junior Biologist jobs near you
Infographic showing various Remote Junior Biologist job openings in the United States as of August 2026, with employment types broken down into 65% Full Time, 30% Part Time, 2% Temporary, and 3% Contract. Highlights an 68% Physical, 2% Hybrid, and 30% Remote job distribution, with an average salary of $89,403 per year, or $43 per hour.

Junior Computational Biologist (Remote)

Astrix Inc

South San Francisco, CA • On-site, Remote

$30 - $34/hr

Full-time

Re-posted 9 days ago

Job description

Pay Rate Low: 30 | Pay Rate High: 34
A leading biotechnology research organization is seeking a Junior Computational Biologist to support efforts in refining how cellular states are quantified and validated!
Title: Jr. Computational Biologist (Remote Contract)
Location: Remote (Must be available during PST business hours)
Compensation: $30-34/hour + benefits
Contract Duration: 6-12+ months
Job Duties:
This project will focus on benchmarking functional scoring methodologies and improving interpretability of high-dimensional transcriptomic datasets.
The selected candidate will contribute to distinguishing true biological signal from technical variation in large-scale single-cell atlases, directly enhancing the reliability of automated cell-state classification frameworks.
Start Date: July 1, 2026
  • Duration: Through December 18, 2026
  • Commitment: Full-time (100%)
  • Ideal Candidate: Upcoming June 2026 PhD graduate or recent PhD graduate
  • Location: Onsite in South San Francisco, CA preferred; remote within the U.S. considered (must work PST hours)
  • Visa Sponsorship: Not availabl

Key Responsibilities
  • Systematically evaluate and benchmark computational approaches for quantifying phenotype activation across single-cell transcriptomic datasets.
  • Establish rigorous statistical baselines and negative-control frameworks to improve the robustness of automated cell-state classification methods.
  • Develop or refine computational methods to address limitations in current approaches.
  • Design strategies to distinguish genuine biological signatures from stochastic or technical noise.
  • Present findings in internal scientific reviews and contribute to potential conference abstracts or peer-reviewed publications.

Required Qualifications
  • Extensive hands-on experience in single-cell data analysis using Scanpy, AnnData, and Pandas.
  • Strong proficiency implementing statistical and machine learning models using scikit-learn and SciPy.
  • Demonstrated commitment to reproducible research practices and well-organized code.
  • Ability to clearly communicate complex computational concepts to interdisciplinary scientific teams.
  • Master's degree with ongoing PhD pursuit, or recent PhD graduate, in Computational Biology, Computer Science, Machine Learning, or related quantitative discipline.
  • Interest in drug discovery and comfort working in dynamic, research-driven environments.

Preferred Qualifications
  • Background knowledge in cell biology and/or immunology.
  • Experience with hypothesis testing, noise modeling, and benchmarking computational tools.
  • Familiarity with Explainable AI (XAI) approaches or large-scale biological datasets.
  • Demonstrated ability to build or extend novel bioinformatics pipelines.
    INDBH
    #LI-MG1

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