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Medical Annotator Jobs (NOW HIRING)

Familiarity with inter-annotator agreement and data quality metrics. * Domain expertise in document ... Everforth Apex offers a range of supplemental benefits, including medical, dental, vision, life ...

Experience as AI reviewer, annotator, or evaluator preferred * Comfortable working with both ... medical condition, genetic characteristics, veteran or marital status, pregnancy, or any other ...

Experience as AI reviewer, annotator, or evaluator preferred * Comfortable working with both ... medical condition, genetic characteristics, veteran or marital status, pregnancy, or any other ...

Experience as AI reviewer, annotator, or evaluator preferred * Comfortable working with both ... medical condition, genetic characteristics, veteran or marital status, pregnancy, or any other ...

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Medical Annotator information

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$13

$76

$192

How much do medical annotator jobs pay per hour?

As of Jun 16, 2026, the average hourly pay for medical annotator in the United States is $76.10, according to ZipRecruiter salary data. Most workers in this role earn between $15.38 and $192.31 per hour, depending on experience, location, and employer.

What are medical annotators?

Medical annotators are professionals who label and categorize medical data—such as images, clinical notes, or audio recordings—to help train artificial intelligence (AI) and machine learning models. Their work ensures that datasets used for developing healthcare technologies are accurate and reliable. Medical annotators often have backgrounds in healthcare, life sciences, or data science, and must be detail-oriented to correctly interpret complex medical information. Their contributions are essential for improving diagnostic tools, automating administrative tasks, and advancing medical research.

What are the typical challenges Medical Annotators face when working with complex clinical data?

Medical Annotators often encounter challenges such as deciphering handwritten notes, interpreting ambiguous medical terminology, and ensuring consistency across large datasets. These challenges require strong attention to detail, familiarity with medical language, and the ability to collaborate with healthcare professionals for clarification. Staying updated on annotation guidelines and maintaining data privacy are also crucial aspects of the role. Overcoming these obstacles is essential for producing high-quality annotated data that supports accurate medical research and AI model development.

What are the key skills and qualifications needed to thrive as a Medical Annotator, and why are they important?

To thrive as a Medical Annotator, you need a solid understanding of medical terminology, anatomy, and clinical documentation, often supported by a background in healthcare or life sciences. Familiarity with annotation tools, electronic health records (EHR) systems, and relevant software like Natural Language Processing (NLP) platforms is typically required. Strong attention to detail, analytical thinking, and effective communication are crucial soft skills in this role. These competencies ensure the accurate labeling of medical data, which is vital for developing reliable AI models and supporting clinical research.

What is the difference between Medical Annotator vs Medical Coder?

AspectMedical AnnotatorMedical Coder
CredentialsTypically requires a healthcare or medical background, sometimes certification in medical terminologyRequires coding certifications like CPC, CCS, or CCS-P
Work EnvironmentOften works in research, AI training, or data annotation settings within healthcare or tech companiesPrimarily works in hospitals, clinics, or billing companies
Industry UsageUsed in medical research, AI development, and data annotation projectsUsed in medical billing, coding, and reimbursement processes

While both roles involve healthcare data, Medical Annotators focus on labeling and annotating medical information for research and AI training, whereas Medical Coders translate medical records into standardized codes for billing and documentation. The roles share some healthcare knowledge requirements but differ in their primary functions and work environments.

More about Medical Annotator jobs
What cities are hiring for Medical Annotator jobs? Cities with the most Medical Annotator job openings:
What states have the most Medical Annotator jobs? States with the most job openings for Medical Annotator jobs include:
Infographic showing various Medical Annotator job openings in the United States as of June 2026, with employment types broken down into 1% As Needed, 77% Full Time, 15% Part Time, and 7% Contract. Highlights an 95% Physical, 1% Hybrid, and 4% Remote job distribution, with an average salary of $158,293 per year, or $76.1 per hour.
Sr Machine Learning Engineer, Tech Lead - Autograder Systems, Evaluation

Sr Machine Learning Engineer, Tech Lead - Autograder Systems, Evaluation

Apple

Cupertino, CA

$181K - $318K/yr

Full-time

Medical, Dental, Retirement

Posted 24 days ago


Apple rating

8.1

Company rating: 8.1 out of 10

Based on 661 frontline employees who took The Breakroom Quiz

6th of 30 rated technology retailers


Job description

We are looking for a Senior MLE Tech Lead to join a centralized evaluation organization and define the next generation of autograder quality across 20+ of Apple's most visible generative AI features. You will own the end-to-end technical vision for how we evaluate model outputs at scale - pioneering state-of-the-art methods, raising the technical bar, and leading a team of talented MLEs to build a robust autograder training and hillclimbing system from the ground up.
This is a high-impact, hands-on leadership role at the intersection of model evaluation, data quality, and ML systems engineering. You will work closely with model developers, data teams, and product partners to ensure our autograders are fast, accurate, and continuously improving - directly shaping the quality of AI experiences used by hundreds of millions of people.
Description
In this role you will focus on:
Technical Leadership
* Define and drive the technical roadmap for autograder quality - researching and introducing novel methods such as reward modeling, LLM-as-judge, preference learning, and calibration techniques to measurably improve evaluation accuracy.
* Architect and lead the build-out of a scalable autograder training pipeline encompassing data curation, model fine-tuning, evaluation harnesses, and versioning.
* Design and own the hillclimbing system that iteratively improves autograder performance through systematic prompt and model optimization loops.
* Establish quality benchmarks, confidence metrics, and failure analysis frameworks that enable the team to track, trust, and act on autograder outputs.
People & Collaboration
* Mentor and technically guide a team of MLEs through design reviews, modeling standards, and hands-on problem-solving - fostering a culture of rigor and continuous learning.
* Partner with data annotation teams to define labeling guidelines that feed autograder training.
* Collaborate with feature engineers to align autograder signals with broader training and product objectives.
* Translate complex technical trade-offs into clear narratives for engineering, product, and leadership audiences.
Preferred Qualifications
Strong ML systems instincts - you care deeply about data quality, reproducibility, latency, and scale.
Background in human-in-the-loop annotation pipelines and inter-annotator agreement analysis.
Prior experience on an evaluation infrastructure or model quality team.
Minimum Qualifications
Master's or PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
5+ years of industry experience in machine learning, with a strong focus on LLM or VLM systems.
Deep expertise in prompt-tuning and fine-tuning techniques (SFT, RLHF, DPO, or equivalent), with proven experience of model calibration and uncertainty estimation.
Familiarity with data flywheel design - leveraging model outputs to continuously improve future training data.
Proficiency in Python and ML frameworks (PyTorch preferred).
Pay & Benefits
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $181,100 and $318,400, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

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About Apple

Sourced by ZipRecruiter

Imagine what you could do here! At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Dynamic, intelligent people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same real passion for innovation that goes into our products also applies to our practices strengthening our dedication to leave the world better than we found it.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Cupertino, CA, US

Year founded

1976