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Data Annotation Biology Jobs (NOW HIRING)

Mercor

Data Science Expert - Fully Remote

New York, NY · Remote

$70 - $100/hr

... Biology , Electrical/Mechanical/Civil Engineering , Physics , Chemistry , Mathematics , Materials ... Prior experience with data annotation, labeling, evaluation, or human feedback collection.

Buck Institute

AI Data Scientist-Furman lab

Novato, CA · On-site

$60K - $75K/yr

Developing systems that assist with literature mining, data annotation, hypothesis generation, and biological interpretation * Evaluating the performance, limitations, and reliability of AI-enabled ...

Mercor

Machine Learning Engineer

New York, NY · Remote

$70 - $100/hr

... Biology , Electrical/Mechanical/Civil Engineering , Physics , Chemistry , Mathematics , Materials ... Prior experience with data annotation, labeling, evaluation, or human feedback collection.

Mercor

AI Researcher

New York, NY · Remote

$70 - $100/hr

... Biology , Electrical/Mechanical/Civil Engineering , Physics , Chemistry , Mathematics , Materials ... Prior experience with data annotation, labeling, evaluation, or human feedback collection.

Mercor

Senior Machine Learning Engineer

New York, NY · Remote

$70 - $100/hr

... Biology , Electrical/Mechanical/Civil Engineering , Physics , Chemistry , Mathematics , Materials ... Prior experience with data annotation, labeling, evaluation, or human feedback collection.

Mercor

STEM Research Scientist - PhD

New York, NY · Remote

$70 - $100/hr

PhD (completed, enrolled, or equivalent research track) in Physics , Chemistry , Biology ... Prior experience with data annotation, labeling, evaluation, or human feedback collection.

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Data Annotation Biology information

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How much do data annotation biology jobs pay per year?

As of Jul 14, 2026, the average yearly pay for data annotation biology in the United States is $122,738.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $136,000.00 per year, depending on experience, location, and employer.

What is the difference between Data Annotation Biology vs Data Labeling Specialist?

AspectData Annotation BiologyData Labeling Specialist
Required CredentialsBiology degree or related certificationHigh school diploma or equivalent, training in labeling tools
Work EnvironmentLaboratory, research settings, or remoteOffice, remote, or data centers
Industry UsageBiotech, healthcare, researchTech, AI, machine learning
Job FocusAnnotating biological data, images, and sequencesLabeling various data types for AI models

Data Annotation Biology involves annotating biological data, often requiring a background in biology, while Data Labeling Specialists focus on labeling diverse data types for AI applications, with less emphasis on biological expertise. Both roles are essential in data preparation but serve different industry needs.

Is it hard to get hired for data annotation?

Getting hired for a data annotation biology role can be straightforward if you have basic attention to detail and familiarity with biological concepts. Many positions require minimal formal education and focus on accuracy and consistency, often with training provided. Competition varies depending on the company and location, but entry-level roles are generally accessible to those with relevant skills.

Does data annotation actually pay you?

Data annotation jobs, including roles in biology, typically pay hourly or per task rates, with compensation varying by company and experience level. Many companies offer remote work with flexible schedules, and some require basic knowledge of biological concepts or annotation tools. Payment is usually processed through standard methods like direct deposit or PayPal.

What is data annotation in biology?

Data annotation in biology involves labeling or tagging biological data—such as images, gene sequences, or medical records—with relevant information to make it useful for research and machine learning. Annotators may identify specific features, mark regions of interest, or classify data according to biological characteristics. This work is crucial for training artificial intelligence systems to recognize patterns, make predictions, and automate analyses in biological research. Annotated datasets help improve the accuracy and reliability of computational models in genomics, microscopy, drug discovery, and more.

What jobs are available in data annotation?

Jobs in data annotation include roles such as data annotator, labeler, or tagger, where individuals review and label data like images, videos, or text to train machine learning models. These positions often require attention to detail, familiarity with annotation tools, and may involve remote work or flexible schedules.

What are the key skills and qualifications needed to thrive as a Data Annotation Biology specialist, and why are they important?

To thrive as a Data Annotation Biology specialist, you need a solid background in biological sciences, attention to detail, and experience handling scientific datasets, often supported by a degree in biology or a related field. Familiarity with annotation tools, bioinformatics databases, and software such as BLAST or Ensembl is typically required, alongside knowledge of data management systems. Strong analytical thinking, precision, and good communication skills help you interpret complex biological data and collaborate effectively with researchers. These skills ensure the accuracy and utility of annotated datasets, which are critical for advancing biological research and data-driven discoveries.

What biology jobs pay over $100k?

In biology-related roles, positions such as biomedical scientists, pharmacologists, and research directors often have salaries exceeding $100,000, especially with advanced degrees and experience. Jobs in biotech companies, pharmaceutical firms, and research institutions tend to offer higher compensation, particularly for those with specialized skills, certifications, or leadership responsibilities.

What are some of the unique challenges faced by data annotators working with biological datasets, and how can they be addressed?

Data annotators in biology often encounter challenges such as dealing with complex, high-dimensional data (like gene sequences or microscopy images) and the need for a deep understanding of biological terminology and context. Errors in annotation can significantly impact downstream research or machine learning models, so maintaining accuracy is crucial. Collaborating closely with biologists and domain experts helps ensure consistency and correctness, while ongoing training and clear annotation guidelines help address ambiguities. Staying up-to-date with evolving biological standards and tools is also essential for success in this role.
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Data Annotation Biology jobs near you
Assistant Research Scientist (PREP0004176)

Assistant Research Scientist (PREP0004176)

Johns Hopkins University

Gaithersburg, MD • On-site

Full-time

Re-posted 6 days ago

Johns Hopkins Medicine rating

7.5

Company rating: 7.5 out of 10

Based on 205 frontline employees who took The Breakroom Quiz

232nd of 884 rated healthcare providers

Job description

Description
PREP Research Associate
This position is part of the National Institute of Standards and Technology (NIST) Professional Research Experience Program (PREP). NIST recognizes that its research staff may want to collaborate with researchers at academic institutions on specific projects of mutual interest and, therefore, requires those institutions to be recipients of a PREP award. The PREP program involves staff from a wide range of backgrounds conducting scientific research across various fields. Individuals in this position will perform technical work supporting the collaboration's scientific research.
Research Title:
Reliability of Human and LLM Annotations for AI Risk Assessment
The work will entail:
This project focuses on using Large Language Models (LLMs) to provide annotations of evaluation data (a.k.a., LLM as judge), and the design of an Inter-Annotator Agreement study to assess the reliability of both human and LLM annotations. The candidate will explore assessing the indicators of a given AI-related risk, determining how to identify them, and providing annotators with examples to annotate the presence of various risks. The project aims to develop an annotation framework for AI risk assessment and establish metrics for data quality in AI risk research, supporting broader work at NIST in assessing and measuring the validity and reliability of AI-related risks in data annotation.
U.S. Citizen Preferred
Key responsibilities will include but are not limited to:
  • Gain familiarity with existing literature on data annotation and LLM as judge
  • Understand NIST's role and ongoing efforts in assessing and measuring the validity and reliability of AI-related risks in data annotation
  • Contribute to developing an annotation framework for AI risk assessment
  • Collaborate effectively with cross-functional and interdisciplinary stakeholders to ensure successful project outcomes

Deliverables
  • Contributions to a NIST report that supports ongoing NIST AI evaluation efforts focused on the design of an Inter-Annotator Agreement to assess the reliability of both human and LLM annotations.

Qualifications
  • Background in Computer Science, Data Science, or related field.
  • Education level: Bachelor's or Graduate Degree
  • Strong interest in data annotation and AI risks
  • Familiarity with scientific reading and technical writing

Application Instructions
Please upload the following with your application:
• CV/Resume
*Please limit C.V to 3 pages only and ONLY include a valid email address for your contact info. Your resume will not be considered if the following information is included on your CV/resume.
• Self portraits
• Phone number
• Home address/Country
• Citizenship status
• Languages spoken
• Sex/Gender
Privacy Act Statement
Authority: 15 U.S.C. § 278g-1(e)(1) and (e)(3) and 15 U.S.C. § 272(b) and (c)
Purpose: The National Institute for Standards and Technology (NIST) hosts the Professional Research Experience Program (PREP) which is designed to provide valuable laboratory experience and financial assistance to undergraduates, post-bachelor's degree holders, graduate students, master's degree holders, postdocs, and faculty.
PREP is a 5-year cooperative agreement between NIST laboratories and participating PREP Universities to establish a collaborative research relationship between NIST and U.S. institutions of higher education in the following disciplines including (but may not be limited to) biochemistry, biological sciences, chemistry, computer science, engineering, electronics, materials science, mathematics, nanoscale science, neutron science, physical science, physics, and statistics. This collection of information is needed to facilitate administrative functions of the PREP Program.
Routine Uses: NIST will use the information collected to perform the requisite reviews of the applications to determine eligibility, and to meet programmatic requirements. Disclosure of this information is also subject to all the published routine uses as identified in the Privacy Act System of Records Notices: NIST-1: NIST Associates.
Disclosure: Furnishing this information is voluntary. When you submit the form, you are indicating your voluntary consent for NIST to use of the information you submit for the purpose stated.

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