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Clinical Annotation Jobs (NOW HIRING)

Verantos

Senior Clinical Informaticist

Specify annotation projects to capture complex clinical ideas. Qualifications Required * MD or DO degree required; completion of an accredited residency program strongly preferred. * Minimum of 2 ...

GE HealthCare

Data Manager - AI Development

$94K - $141K/yr

This role works closely with AI/ML engineers to define data needs for AI features, coordinates with internal and external data collection teams/clinical team, oversees annotation activities, and ...

Veracyte

Senior Bioimage Scientist

San Diego, CA · On-site

$142K - $156K/yr

... clinical hypotheses (e.g., immune contexture, tumor aggressiveness, treatment response) with explainable image-based measurements. * Work closely with pathologists to define annotation schemas ...

SmarterDx

Clinical Coding Specialist

$75K - $105K/yr

Familiarity with evaluation frameworks, labeling, or annotation workflows * Certified Clinical Documentation Specialist (CCDS) or Clinical Documentation Improvement Practitioner (CDIP) credentials ...

Cook Children's

Bioinformatician

Fort Worth, TX · On-site

... annotation and interpretation, building tools for clinical decision support, and advancing data integration across genomics, clinical phenotypes, and functional datasets. This role supports precision ...

Intuitive

Lead Cytopathologist

San Carlos, CA · On-site

You will provide medical and scientific oversight for dataset curation, annotation strategy, and algorithm evaluation, ensuring that our products are clinically robust and aligned with real-world ...

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Clinical Annotation information

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$14

$34

$90

How much do clinical annotation jobs pay per hour?

As of Jul 7, 2026, the average hourly pay for clinical annotation in the United States is $34.62, according to ZipRecruiter salary data. Most workers in this role earn between $16.59 and $32.93 per hour, depending on experience, location, and employer.

What does an annotation job do?

A clinical annotation job involves reviewing and labeling medical data, such as patient records or imaging, to ensure accurate and consistent information for research and development. Annotators typically use specialized tools and must follow strict guidelines to support machine learning models and clinical studies.

What is a clinical annotator?

A clinical annotator is a professional who reviews and labels medical data, such as electronic health records or medical images, to help train machine learning algorithms for healthcare applications. This role requires attention to detail, knowledge of medical terminology, and often involves using specialized annotation tools. Accurate annotations are essential for developing reliable clinical AI systems.

What are some common challenges faced by professionals in Clinical Annotation roles, and how can they be addressed?

Professionals in Clinical Annotation often encounter challenges such as interpreting complex medical terminology, maintaining accuracy while managing large volumes of data, and keeping up with evolving clinical guidelines. To address these challenges, it's important to stay current with medical literature, participate in regular training, and collaborate closely with clinical experts and data scientists. Utilizing standardized annotation tools and clear protocols can also help ensure consistency and quality in the annotation process.

What is the difference between Clinical Annotation vs Clinical Data Analyst?

AspectClinical AnnotationClinical Data Analyst
Required CredentialsTypically requires life sciences or healthcare background, often with certifications in clinical research or data managementRequires degrees in statistics, data science, or related fields; certifications in data analysis tools are common
Work EnvironmentWorks primarily in research settings, hospitals, or biotech companies, focusing on annotating clinical dataWorks in healthcare, research, or pharmaceutical companies analyzing clinical data sets
Employer & Industry UsageUsed in clinical research, drug development, and medical data curationUsed across healthcare, research, and pharmaceutical industries for data analysis and reporting

While both roles involve working with clinical data, Clinical Annotation focuses on labeling and curating data for accuracy, whereas Clinical Data Analysts interpret and analyze data to generate insights. Both roles are essential in clinical research but serve different functions within the data lifecycle.

What is clinical annotation?

Clinical annotation is the process of labeling and adding relevant medical information to clinical data, such as patient records, images, or genetic information, to provide context and meaning. This process helps researchers and healthcare professionals interpret and analyze data more effectively, supporting advancements in diagnostics, treatment, and medical research. Clinical annotation is essential for developing machine learning models in healthcare and ensuring data quality in clinical studies.

What is a medical annotation?

A medical annotation involves adding detailed labels or notes to medical data, such as images, clinical notes, or electronic health records, to identify specific features or conditions. Clinical annotators use specialized tools and follow guidelines to ensure accuracy, supporting machine learning models and medical research.

What are the key skills and qualifications needed to thrive as a Clinical Annotation Specialist, and why are they important?

To thrive as a Clinical Annotation Specialist, you need a background in life sciences or healthcare, knowledge of medical terminology, and attention to detail, often supported by a relevant degree or certification. Familiarity with annotation platforms, EHR systems, and coding standards like ICD or SNOMED CT is typically required. Strong analytical thinking, communication, and collaboration skills help ensure accuracy and efficiency in interpreting and labeling clinical data. These competencies are crucial for producing high-quality annotated datasets that support medical research, AI development, and improved patient care.

How much do annotators get paid?

Clinical annotators typically earn between $12 and $20 per hour, depending on experience, location, and the complexity of the annotation tasks. Some positions may offer fixed project-based pay or part-time schedules, often requiring attention to detail and familiarity with medical terminology.
Senior Clinical Informaticist

Senior Clinical Informaticist

Verantos

Remote

Full-time

Re-posted 8 days ago

Job description

Overview
Verantos (https://verantos.com) is the global leader in high-validity real-world evidence (RWE) for life sciences. By incorporating robust clinical narrative data, artificial intelligence (AI) technology, and measured validity, Verantos is the first company to generate research-grade evidence at scale across therapeutic areas.
The Verantos Evidence Platform integrates heterogeneous real-world data sources and generates evidence with the accuracy required for market access, health economics and outcomes research (HEOR), medical affairs, and regulatory use. Leveraging data science, AI, and advanced data sources such as electronic health records (EHRs), the platform supports complex clinical studies across multiple therapeutic areas. Today, some of the largest biopharma companies in the world are Verantos customers.
As a Senior Clinical Informaticist, you will help shape how clinical data is transformed and made usable for research by leading efforts in knowledge management, semantic normalization, and data quality. You will collaborate with clinicians, scientists, data scientists, product managers, and engineers to define scalable approaches for mapping clinical concepts, identify where and how key concepts can be captured, develop concept lists for cohort creation, and design clinical data quality checks to ensure datasets meet clinical expectations.
Your work will directly support high-impact research by ensuring that the right concepts are captured, standardized, and accessible across diverse data sources. This is an opportunity to work at the intersection of clinical insight and technical implementation, transforming complex healthcare data into research-grade evidence that advances decision-making at scale.
Responsibilities
Knowledge management
  • Create and maintain concept groups.
  • Review customer code lists and recommend updates as needed.
  • List and define critical variables for each pragmatic registry.

Semantic normalization
  • Map claims data and health system data to standard concepts in the OMOP Common Data Model (CDM).
  • Semantically normalize CDM-converted data coming from multiple different health systems and map high priority unmapped concepts

Data usability
  • Create documentation on how to best use data to generate insights.
  • Collaborate with customers to help them understand how to leverage data to answer research questions.

Data Quality
  • Define disease-area-specific data quality testing processes.

Concept Identification
  • Determine how to best capture concepts needed to answer customer research questions and analyze prevalence of concepts for feasibility assessments.
  • Specify annotation projects to capture complex clinical ideas.

Qualifications
Required
  • MD or DO degree required; completion of an accredited residency program strongly preferred.
  • Minimum of 2 years of clinical experience required.
  • Experience working with standard clinical vocabularies such as SNOMED CT, LOINC, RxNorm, ICD-10-CM, CPT, or HCPCS, including an understanding of their structure and use in representing clinical data.
  • Familiarity with EHR systems and common clinical documentation practices.
  • Strong understanding of data captured in structured EHRs, unstructured EHRs (e.g., clinical notes), and claims datasets, including which types of concepts each source best captures.
  • Excellent verbal and written communication skills for cross-functional collaboration.

Preferred
  • Advanced training or certification in clinical informatics.
  • Minimum of 2-3 years of experience in clinical informatics or a closely related role.
  • Demonstrated expertise in semantic mapping of source clinical terms to standard vocabularies (e.g., SNOMED CT, LOINC, RxNorm, ICD-10-CM, CPT, HCPCS).
  • Demonstrated analytical and problem-solving skills applied to complex clinical data challenges, such as resolving semantic ambiguity, aligning heterogeneous data sources, or developing scalable mapping solutions.
  • Experience defining and reviewing data quality tests on clinical datasets.
  • Experience with the OMOP Common Data Model.
  • Proficiency with terminology mapping tools.
  • Familiarity or experience with AI-based extraction from unstructured clinical notes.
  • Strong understanding of the hierarchical structure and relationships in standard clinical terminologies.
  • Proficiency in tools for data analysis or transformation (e.g., SQL, Excel, Python, or R).
  • Prior involvement in projects involving phenotyping or computable cohort definitions.
  • Proven ability to collaborate with technical teams to design and implement repeatable, scalable approaches to knowledge-driven workflows.
  • AI-first mindset, with a focus on leveraging automation to develop scalable, repeatable solutions for clinical data normalization, concept identification, and quality assessment.

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