ZipRecruiter

Log In

1

Medical Annotation Jobs in Washington (NOW HIRING)

Welocalize

Data Labeling Associate

Washington, DC

$34/hr

WHAT YOU'LL DO • Execute Data labelling and annotation tasks across speech and voice datasets ... Medical, Dental, and Vision Insurance • Free Breakfast, Lunch, and Dinner (where applicable) • ...

Welocalize

Data Labeling Associate

Washington, DC

$34/hr

WHAT YOU'LL DO • Execute Data labelling and annotation tasks across speech and voice datasets ... Medical, Dental, and Vision Insurance • Free Breakfast, Lunch, and Dinner (where applicable) • ...

next page

Showing results 1-20

All JobsMedical Annotation JobsMedical Annotation Jobs in Washington

Medical Annotation information

See Washington salary details

$53.8K

$87.7K

$118.4K

How much do medical annotation jobs pay per year?

As of Jun 13, 2026, the average yearly pay for medical annotation in Washington is $87,672.00, according to ZipRecruiter salary data. Most workers in this role earn between $73,100.00 and $101,900.00 per year, depending on experience, location, and employer.

What does a medical annotator do?

A medical annotator reviews and labels medical data such as images, clinical notes, and reports to help train machine learning models for healthcare applications. They ensure data accuracy and consistency, often using specialized tools and following strict guidelines to support medical AI development.

What is a Medical Annotation job?

A Medical Annotation job involves labeling and categorizing medical data, such as patient records, images, or clinical notes, to train AI models in healthcare applications. Annotators ensure that data is accurately tagged for use in machine learning, often working with radiology scans, electronic health records, or biomedical texts. This role requires attention to detail and may involve domain knowledge in medicine or life sciences to ensure high-quality annotations.

What qualifications do you need for data annotation?

Medical annotation roles typically require a high school diploma or equivalent, with some positions preferring a background in healthcare, biology, or related fields. Attention to detail, good communication skills, and familiarity with annotation tools or software are important; certifications in medical coding or data management can be advantageous.

What are the key skills and qualifications needed to thrive in the Medical Annotation position, and why are they important?

To thrive in Medical Annotation, you need a strong background in life sciences or health care, familiarity with medical terminology, and acute attention to detail. Experience with annotation platforms, electronic health records (EHRs), and possibly certifications in medical coding or data annotation are often expected. Excellent communication, analytical thinking, and the ability to follow structured guidelines are standout soft skills. These competencies are essential to ensure the accuracy, consistency, and reliability of annotated medical data used in research, AI training, or clinical analysis.

What are the typical daily responsibilities of someone working in Medical Annotation?

In Medical Annotation, your day-to-day work often involves reviewing and labeling various types of medical data, such as clinical notes, radiology images, or laboratory reports, according to strict guidelines. You may collaborate with data scientists, healthcare professionals, or other annotators to ensure accuracy and resolve ambiguities. Attention to detail is crucial, as your annotations directly support the training of AI systems or research projects. Regular feedback sessions and audits are common to maintain high-quality standards. This role offers a mix of independent work and teamwork, fostering both focus and professional growth.

Can you actually make money with data annotation?

Medical annotation jobs are paid positions that offer income based on the volume and complexity of tasks completed, often on a freelance or part-time basis. Earnings can vary widely depending on experience, skill level, and the employer, with some annotators earning a modest income while others with specialized skills can earn more. Consistent work and proficiency with annotation tools can improve earning potential.

How to become a medical annotator?

To become a medical annotator, candidates typically need a background in healthcare, life sciences, or related fields, along with strong attention to detail and familiarity with medical terminology. Training is often provided by employers, and proficiency in using annotation tools or software is beneficial. Some positions may require a certification or degree in a relevant discipline, and the work can be performed remotely or on-site.
What are popular job titles related to Medical Annotation jobs in Washington? For Medical Annotation jobs in Washington, the most frequently searched job titles are:
What job categories do people searching Medical Annotation jobs in Washington look for? The top searched job categories for Medical Annotation jobs in Washington are:
What cities in Washington are hiring for Medical Annotation jobs? Cities in Washington with the most Medical Annotation job openings:
Medical Annotation jobs near you
Infographic showing various Medical Annotation job openings in Washington as of June 2026, with employment types broken down into 1% As Needed, 77% Full Time, 15% Part Time, and 7% Contract. Highlights an 90% Physical, 1% Hybrid, and 9% Remote job distribution, with an average salary of $87,672 per year, or $42.1 per hour.
AI Engineer - AI+CryoET

AI Engineer - AI+CryoET

Howard Hughes Medical Institute (HHMI)

Ashburn, VA • On-site

Full-time

Posted 28 days ago

Job description

Job Summary:
Howard Hughes Medical Institute (HHMI) is investing significantly to support AI-driven projects in scientific research. The AI Engineer role involves developing AI methods for 3D particle detection and structural analysis in cryo-electron tomography data, collaborating closely with experts across multiple institutions.
Responsibilities:
• Develop and evaluate deep learning models for detecting and localizing gold nanoparticles and macromolecular particles (e.g., nucleosomes, synaptic receptors) in cryoET data, and for identification of nucleosome arrangement and connectivity in chromatin.
• Develop methods to leverage gold nanoparticle detections to improve tomogram reconstruction, addressing challenges in tilt-series alignment, deformations, and low signal-to-noise conditions.
• Design and execute rigorous AI model training and evaluation pipelines, including proper handling of missing wedge artifacts, CTF effects, and sim-to-real transfer from MD-derived synthetic training data.
• Identify where additional human annotation and proofreading will be most helpful and design and guide annotation efforts.
• Contribute to scientific publications, present findings at conferences, and maintain a well-documented codebase enabling seamless reproduction and extension of results.
• Collaborate with interdisciplinary teams across multiple institutions.
Qualifications:
Required:
• Master's or PhD in Computer Science, Applied Mathematics, Physics, Computational Chemistry, or a related field, or equivalent combination of education and experience.
• 3+ years training and evaluating deep learning models, particularly on 3D or volumetric image data. Experience with detection, segmentation, or inverse problems in imaging is strongly preferred.
• Strong Python skills, and proficiency in PyTorch and/or JAX. Ability to reason about neural network behavior from first principles: how architectural choices, regularization, and training procedures affect model behavior.
• Rigorous experimental design: model comparisons, ablation studies, reproducibility.
• Commitment to open science.
• Experience with scalable GPU-based computing environments on Linux HPC clusters and high-throughput processing for large-scale data.
• Excellent communication skills and keen interest in working in a truly interdisciplinary environment.
Preferred:
• Experience with cryo-EM/ET data processing, tomographic reconstruction, or related inverse problems in imaging.
• Familiarity with molecular dynamics simulations (e.g., OpenMM, LAMMPS) and/or synthetic data generation for training ML models.
• Experience with differentiable rendering, neural radiance fields, or analysis-by-synthesis approaches for 3D reconstruction.
• Knowledge of cryoET software tools (IMOD, Warp, RELION, AreTomo etc.) or microscopy data formats (MRC, Zarr).
• Experience with template matching, sub-tomogram averaging, or particle picking in cryo-EM/ET contexts.
Company:
Founded in 1953, HHMI invests in scientists at all career stages who make discoveries that advance human health and our fundamental understanding of biology. Founded in 1953, the company is headquartered in Chevy Chase, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

Apply