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Medical Annotation Jobs in Washington (NOW HIRING)

Welocalize

Data Labeling Associate

Washington, DC · On-site

$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) • ...

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Medical Annotation information

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. Attention to detail and knowledge of medical terminology are essential for this role.

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.

How much do annotators get paid?

Medical annotators typically earn between $12 and $25 per hour, depending on experience, complexity of tasks, and the employer. Some positions offer fixed project-based rates or part-time schedules, and familiarity with annotation tools and medical terminology can influence pay rates.

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 where individuals label medical data for AI training, and earnings depend on factors like experience, workload, and platform. While some annotators earn a steady income, pay rates vary widely, and it is generally considered a part-time or freelance opportunity rather than a full-time career. Developing skills in relevant tools and maintaining accuracy can improve earning potential.

What qualifications do I need for data annotation?

For medical annotation roles, candidates typically need a background in healthcare, life sciences, or related fields, along with strong attention to detail and familiarity with medical terminology. Basic computer skills and experience with annotation tools or software are also important. Some positions may require certification or training in medical coding or data management.
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Medical Annotation jobs near you
Infographic showing various Medical Annotation job openings in Washington as of June 2026, with employment types broken down into 33% Full Time, 33% Part Time, and 34% Contract. Highlights an 67% In-person, and 33% Remote job distribution.
AI Engineer - AI+CryoET

AI Engineer - AI+CryoET

Howard Hughes Medical Institute (HHMI)

Ashburn, VA • On-site

Full-time

Posted 18 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.

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