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Data Annotation Jobs in Leesburg, VA (NOW HIRING)

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Data Annotation information

What is a data annotation?

A Data Annotation job involves labeling and categorizing data, such as text, images, audio, or video, to help train machine learning models. Annotators apply tags, bounding boxes, or classifications to data based on specific guidelines. This process improves the accuracy of AI systems in recognizing patterns and making predictions. Many data annotation jobs require attention to detail and familiarity with specific domains. It is commonly used in applications like autonomous driving, natural language processing, and computer vision.

What does a data annotation do?

A typical workday as a Data Annotator involves reviewing datasets—such as images, audio, text, or video—and accurately labeling or categorizing information according to specific project guidelines. Most Data Annotators work independently, but they often collaborate with project managers or data scientists to clarify requirements and resolve ambiguities. Tasks may be repetitive, but adhering to precise standards is vital for maintaining data quality. Work environments can range from technology companies to remote or freelance settings, and advancement opportunities exist as team leads or quality assurance specialists for those who excel in consistency and reliability.

What are the key skills and qualifications needed to thrive in data annotation?

To thrive in Data Annotation, you need strong attention to detail, accuracy, and basic data handling skills, often supported by a high school diploma or equivalent. Familiarity with annotation platforms, data labeling software, or content management systems is frequently required, though specific certifications are rare. Excellent communication, time management, and the ability to focus on repetitive tasks distinguish top performers in this role. These skills are crucial because accurate and consistent data annotation directly impacts the quality of machine learning models and AI applications.

How much money can I make doing data annotation?

Data annotation jobs typically pay between $10 and $20 per hour, depending on the complexity of the task and the employer. Experienced annotators or those working on specialized projects may earn higher rates, especially if they have skills in specific tools or domains. Earnings can vary based on whether the work is freelance, part-time, or full-time, and some platforms offer bonuses for accuracy or speed.

What are the most commonly searched types of Data Annotation jobs in Leesburg, VA?

The most popular types of Data Annotation jobs in Leesburg, VA are:

What are popular job titles related to Data Annotation jobs in Leesburg, VA?

For Data Annotation jobs in Leesburg, VA, the most frequently searched job titles are:

What job categories do people searching Data Annotation jobs in Leesburg, VA look for?

The top searched job categories for Data Annotation jobs in Leesburg, VA are:

What cities near Leesburg, VA are hiring for Data Annotation jobs?

Cities near Leesburg, VA with the most Data Annotation job openings:

Infographic showing various Data Annotation job openings in Leesburg, VA as of August 2026, with employment types broken down into 1% As Needed, 83% Full Time, 14% Part Time, and 2% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution.

$90 - $120/hr

Other

Re-posted 7 days ago


Job description

About the Role

The position is part of the AI+CryoET project at HHMI, focused on developing AI methods for particle detection and structural analysis in cryo-electron tomography (cryoET) data. The role involves collaborating with experimental and computational scientists at several institutions to create supervised and self-supervised model architectures that can detect gold‑nanoparticle probes, identify nucleosome arrangements, and improve tomogram reconstructions.

Responsibilities

• Develop and evaluate deep‑learning models for detecting and localizing gold nanoparticles and macromolecular particles (e.g., nucleosomes, synaptic receptors) in cryoET data.• Design methods that use gold‑nanoparticle detections to improve tomogram reconstruction, addressing challenges such as tilt‑series alignment, deformations, and low signal‑to‑noise conditions.• Build rigorous AI training and evaluation pipelines, including handling of missing‑wedge artifacts, CTF effects, and sim‑to‑real transfer from molecular‑dynamics‑derived synthetic training data.• Identify where additional human annotation and proofreading will be most helpful and guide annotation efforts.• Contribute to scientific publications, present findings at conferences, and maintain a well‑documented codebase that enables reproducibility and extension of results.• Collaborate with interdisciplinary teams across multiple institutions.

Qualifications
  • Master’s or PhD in Computer Science, Applied Mathematics, Physics, Computational Chemistry, or a related field, or an equivalent combination of education and experience.
  • 3+ years training and evaluating deep‑learning models, especially 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 skills (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 interest in interdisciplinary collaboration.
  • Optional: experience with cryo‑EM/ET data processing, tomographic reconstruction, or related inverse problems; familiarity with molecular‑dynamics simulations (OpenMM, LAMMPS); knowledge of cryoET software tools (IMOD, Warp, RELION, AreTomo) or file formats (MRC, Zarr); experience with template matching or sub‑tomogram averaging; familiarity with differentiable rendering or neural radiance fields.
Benefits
  • Competitive compensation package with comprehensive health and welfare benefits.
  • Supportive team environment that promotes collaboration and knowledge sharing.
  • Access to world‑class computational infrastructure, GPU‑based computing environments, and unique high‑quality cryoET datasets.
  • Opportunities to work directly with leading structural biologists, cryoET experimentalists, and molecular‑dynamics experts on highly interdisciplinary projects.
  • Work‑life balance amenities such as on‑site childcare, free gyms, on‑campus housing, social and dining spaces, and a shuttle bus service to Janelia from the Washington, D.C. metro area.
  • Partnership with frontier AI labs on scientific applications of AI.
Equal Opportunity Employer

HHMI is an Equal Opportunity Employer. We employ a rigorous process to evaluate and provide reasonable accommodations for all applicants.

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