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Clinical Annotation Jobs (NOW HIRING)

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 ...

Philips International

Clinical Development ScientistSkip to main content# CareersClinical Development Scientist page is ... annotation integrity, adherence to protocol and traceability, while resolving complex data ...

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 ...

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 ...

Phoenix Court Group

$59K - $80K/yr

Co‑develop the clinical components of evaluation rubrics, acceptance criteria, release thresholds, and annotation requirements. * Define the clinical content needed for annotator training ...

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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 Sep 13, 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 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 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.

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 other helpful pages are available for Clinical Annotation?

Other pages related to Clinical Annotation:

Infographic showing various Clinical Annotation job openings in the United States as of September 2026, with employment types broken down into 3% As Needed, 73% Full Time, 16% Part Time, and 8% Contract. Highlights an 84% Physical, 1% Hybrid, and 15% Remote job distribution, with an average salary of $72,002 per year, or $34.6 per hour.

Systems Engineer III (Research)

San Francisco, CA • On-site

Heartflow
Medical Equipment and Supplies Manufacturing • 201 - 500 employees
Learn more about Heartflowexternal

$150K - $185K/yr

Full-time

Posted 8 days ago

HeartFlow rating

7.2

Company rating: 7.2 out of 10

Based on 11 frontline employees who took The Breakroom Quiz

Job description

We are looking for a Systems Engineer III to own Heartflow's research data pipeline. In this role, you will generate the data that enables current and future algorithm and product research. You will partner with Data Scientists, Research Scientists, and engineering teams to understand our products, systems, and constraints, then determine how future data campaigns should be collected, annotated, and analyzed.

Trustworthy data is fundamental to everything we do at Heartflow. It informs our research and helps us improve and transform our algorithms and products. In this role, your work will turn complex clinical and product questions into reliable, well-documented datasets that move research forward. If you are drawn to solving systematic problems, passionate about research, and deeply committed to high-quality data, then this is the role for you.

Key Responsibilities

  • Campaign Design & Execution: Own data campaigns for research. Define labeling objectives, annotation schemas, sample selection and inclusion criteria, and acceptance criteria in partnership with Data and Research Scientists. Plan and track timelines and resourcing to drive each campaign from ideation to delivery.
  • Annotation Process & Tooling: Own the annotation process end to end, including the workflow, software, and steps that move data through it (ingest, schema mapping, format and ontology validation, and delivery). Select and configure the right tools, and build a reproducible process that removes bottlenecks and scales campaigns across diverse research objectives.
  • Ground-Truth Quality & Reproducibility: Define and monitor quality across data campaigns. Distinguish annotation noise from genuine clinical disagreement, and quantify the reliability of the data you deliver.
  • Cross-Functional Collaboration: Partner with Data Scientists, Research Scientists, Process Engineering, Product, Clinical, and Regulatory teams. Turn campaigns into artifacts other functions consume: documented datasets, quality reports, and dataset records reproducible and auditable enough to support algorithm development and product decisions.
  • Research Initiative & Data Planning: Translate research initiatives into clear data requirements, including target populations, modalities, labels, quality thresholds, and delivery timelines. Trace each requirement to a planned or active campaign, identify gaps and dependencies early, and coordinate priorities to ensure the right data is available when research needs it.
  • Systems Engineering Best Practices: Promote Systems Engineering best practices across the organization by sharing methods, improving processes, and strengthening systems thinking in data and research initiatives.

Required Qualifications

  • Education: Bachelor's or Master's degree (Ph.D. preferred) in Systems Engineering, Computer Science, Data Science, Biomedical Engineering, or a related field.
  • Experience: 5+ years of industry experience in research, systems engineering, or a related field, ideally in medical imaging, medical devices, or another regulated domain.
  • Program & Delivery Ownership: Proven track record translating complex projects or research needs into clear, executable programs and driving them from planning through delivery. Experience coordinating cross-functional teams and delivering complete, documented outputs.
  • Ground-Truth & Quality Methods: Working knowledge of methods for evaluating data and experiment quality, including reproducibility, agreement, and error detection, along with the judgment to distinguish protocol or annotation noise from true clinical variability.
  • Medical Imaging Fluency: Solid understanding of medical image data structures and imaging workflows (e.g., DICOM), as well as the clinical realities that make annotation challenging.
  • Data & Tooling Proficiency: Proficiency in Python and SQL for building and validating data pipelines, computing quality metrics, and producing dataset documentation.
  • Communication: Exceptional ability to translate research requirements into executable annotation campaigns, and to report dataset quality and provenance clearly to cross-functional stakeholders.

Preferred Qualifications

  • Experience running annotation or ground-truth campaigns for medical imaging AI, especially CT or cardiovascular imaging (CCTA).
  • Familiarity with reproducibility and agreement statistics (e.g., intraclass correlation, Bland-Altman) as they apply to reader studies and ground truth.
  • Experience with large-scale data querying using SQL and cloud storage such as AWS.
  • Experience developing SaMD products and contributing to regulatory filings.

A reasonable estimate of the base salary compensation range is $150,000 to $185,000, bonus, and equity. #LI-IB1

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