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Medical Image Annotation Jobs in Washington (NOW HIRING)

Knowledge and experience in biomedical informatics, image processing, and natural language ... Experience in content development and/or text annotation (e.g., annotation of certain types of ...

Knowledge and experience in biomedical informatics, image processing, and natural language ... Experience in content development and/or text annotation (e.g., annotation of certain types of ...

Software Engineer II

Herndon, VA · On-site

$100K - $137K/yr

Build and implement evaluation frameworks for multimodal model performance, including image ... Competitive options for Medical, Dental and Vision insurance * Employer-paid short-term and ...

Medical Image Annotation information

What is medical image annotation?

Medical image annotation is the process of labeling or marking specific structures, regions, or abnormalities in medical images such as X-rays, CT scans, or MRIs. These annotations are crucial for training artificial intelligence (AI) models to assist in diagnostics, research, and treatment planning. Expert annotators, often with medical backgrounds, use specialized software to ensure accuracy and consistency. This work helps improve the performance of AI systems in identifying diseases and supporting healthcare professionals.

What are some common challenges faced by professionals in medical image annotation roles, and how can they be addressed?

Medical image annotation professionals often encounter challenges such as interpreting complex or ambiguous images, ensuring consistency across annotations, and keeping up with evolving medical guidelines. To address these challenges, many teams implement standardized protocols, regular training sessions, and peer review systems to maintain accuracy and reliability. Collaboration with radiologists and other medical experts is also common, allowing annotators to clarify uncertainties and improve the quality of annotations over time.

What is the difference between Medical Image Annotation vs Medical Data Labeling?

AspectMedical Image AnnotationMedical Data Labeling
Required CredentialsBasic understanding of medical imaging, attention to detailSimilar, often no formal certification needed
Work EnvironmentMedical imaging platforms, annotation toolsData management systems, labeling software
Industry UsageHealthcare, medical AI developmentHealthcare, medical AI, data analysis
Search & Comparison IntentYes, often compared for AI training rolesYes, related but broader in data types

Medical Image Annotation involves marking specific regions or features in medical images like X-rays or MRIs to train AI models. Medical Data Labeling encompasses annotating various medical data types, including images, text, and reports. While both roles support medical AI development, Image Annotation is specialized in visual data, whereas Data Labeling covers a wider range of medical information.

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

To excel as a Medical Image Annotation Specialist, you need a solid understanding of medical imaging modalities, anatomy, and basic clinical terminology, often supported by relevant education or experience in healthcare or life sciences. Familiarity with annotation software, image processing tools, and sometimes specialized platforms like DICOM viewers is typically required. Attention to detail, precision, and effective communication are crucial soft skills for ensuring accuracy and collaborating with clinical or research teams. These competencies are vital because high-quality, accurate annotations directly impact the development of AI models and the reliability of diagnostic tools in healthcare.
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AI Engineer - AI+CryoET

Full-time

Posted 17 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.