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From Home Medical Data Annotation Jobs (NOW HIRING)

... C funding from Wellington Management, CRV, Next47 and Y Combinator. The role As a Human Data Operations Strategist, you will play a critical role in managing and optimising data annotation and ...

Technical Program Manager, Data Engine

Redwood City, CA · On-site

$157K - $204K/yr

... data annotation or collection • Ability to leverage AI to help improve productivity Company : Sunday is a robotics and artificial intelligence company that develops an autonomous home robot to ...

Data Operations Engineer

Mountain View, CA · On-site

$136K - $163K/yr

... questions from engineering, product, and business teams. • Translate ambiguous or high-level ... with data annotation, labeling workflows, or dataset preparation for machine learning. • ...

Data Labeler

$30K - $50K/yr

We've raised $11M from top funds, founders, and senior leaders at OpenAI, Anthropic, HuggingFace ... Help improve evaluation guidelines and annotation processes * Contribute to the datasets used to ...

Showing results 21-40

From Home Medical Data Annotation information

What are the key skills and qualifications needed to thrive as a from home medical data annotation specialist, and why are they important?

To succeed as a From Home Medical Data Annotation Specialist, you need a background in medical terminology, attention to detail, and familiarity with clinical data formats, often supported by relevant coursework or experience in healthcare or data management. Proficiency with data annotation platforms, medical coding systems (such as ICD-10 or CPT), and secure remote work tools is frequently required. Strong soft skills include self-motivation, time management, and clear written communication to ensure accuracy and meet deadlines independently. These skills are crucial for producing high-quality, reliable annotated data that supports medical research, AI model development, and healthcare decision-making.

What is a from home medical data annotation job?

A work from home medical data annotation job involves labeling and categorizing medical data, such as images, text, or audio, to help train artificial intelligence systems used in healthcare. This typically means identifying and tagging important information within medical records, radiology images, or clinical notes so that machine learning models can better understand and process the data. These roles are mostly remote, allowing individuals to work from their own homes while contributing to the development of advanced healthcare technologies. Attention to detail and a basic understanding of medical terminology are often required.

What are the main challenges of working as a medical data annotator from home, and how can they be addressed?

One of the main challenges of working as a medical data annotator from home is maintaining consistent focus and accuracy when handling large volumes of sensitive patient data. Distractions at home, limited direct supervision, and potential technology issues can also impact productivity. To address these, it's important to establish a dedicated workspace, follow strict data security protocols, regularly communicate with your team, and utilize project management tools to track progress. Many employers also provide training and ongoing support to help remote annotators stay compliant with privacy regulations and quality standards.

What is the difference between From Home Medical Data Annotation vs Medical Data Labeler?

AspectFrom Home Medical Data AnnotationMedical Data Labeler
CredentialsBasic computer skills, attention to detailSimilar credentials, often no formal certification required
Work EnvironmentRemote, home-basedRemote, home-based
Industry UsageHealthcare, AI trainingHealthcare, AI, machine learning
Job FocusAnnotating medical images and data for AI modelsLabeling medical data for machine learning algorithms

Both roles involve remote work and require attention to detail, focusing on medical data annotation and labeling for AI applications. The main difference lies in terminology; 'From Home Medical Data Annotation' emphasizes the annotation process, while 'Medical Data Labeler' highlights the labeling aspect. Both positions are essential in healthcare AI development and share similar credentials and work environments.

What jobs in healthcare allow you to work from home?

Healthcare jobs like medical data annotation, telehealth consulting, medical coding, and remote patient support allow professionals to work from home. These roles typically require specialized training, attention to detail, and familiarity with healthcare software or electronic health records systems.
More about From Home Medical Data Annotation jobs
What cities are hiring for From Home Medical Data Annotation jobs? Cities with the most From Home Medical Data Annotation job openings:
What are the most commonly searched types of Medical Data Annotation jobs? The most popular types of Medical Data Annotation jobs are:
What states have the most From Home Medical Data Annotation jobs? States with the most job openings for From Home Medical Data Annotation jobs include:
Infographic showing various From Home Medical Data Annotation job openings in the United States as of July 2026, with employment types broken down into 3% Locum Tenens, 23% Full Time, 28% Part Time, 1% Contract, 44% Nights, and 1% Summer. Highlights an 46% Physical, 1% Hybrid, and 53% Remote job distribution.

Human Data Operations Strategist

Encord

New York, NY • On-site

Full-time

Medical, Dental, Vision, PTO

Posted 15 days ago


Job description

About us

Encord is the universal data layer for AI that helps 300+ AI teams train and run models on the right data. Our platform indexes, curates, annotates, and evaluates data across the full AI lifecycle, from development through production.

 

Trusted by Woven by Toyota, AXA, UiPath, Zipline, and more. We're an ambitious team of 100+ working at the frontier of AI and have raised $60M in Series C funding from Wellington Management, CRV, Next47 and Y Combinator.

 

The role

As a Human Data Operations Strategist, you will play a critical role in managing and optimising data annotation and machine learning workflows for our clients. You will work closely with cross-functional teams, including clients, annotation specialists, and machine learning engineers, to ensure high-quality data is available for AI models.

What you'll do

  • Oversee data annotation projects, translating complex AI and machine learning requirements into clear workflows and instructions for data annotation teams

  • Ensure the highest standards of data quality by designing and refining annotation processes, auditing results, and implementing feedback loops

  • Act as a trusted advisor to clients, helping them design and implement the best data annotation workflow for their human annotation process

  • Provide guidance and feedback to the annotation team, ensuring team members are equipped with the context and skills needed to perform high-quality work aligned with project requirements and best practices

  • Work closely with product and engineering teams to drive improvements in AI training data processes, tools, and methodologies

Who we're looking for

  • A sharp, execution-oriented operator with a consulting or AI company pedigree — you bring structured thinking, strong project management instincts, and a bias for getting things done

  • Analytically rigorous and comfortable with ambiguity — you break down complex operational challenges from first principles and build clear, actionable plans to solve them

  • Technically fluent enough to get hands-on with data — whether that's querying a database, auditing annotation outputs, or automating a workflow in Python

  • Passionate about AI and machine learning, with genuine curiosity about how data quality and operations underpin model performance

  • A natural communicator who can translate fluidly between ML engineers and non-technical clients, keeping complex multi-stakeholder projects on track

  • Entrepreneurial and collaborative — you thrive in fast-paced environments and take ownership without waiting to be told what to do

Experience requirements

  • 3–7 years of professional experience, with a strong preference for backgrounds in top-tier strategy consulting and/or operations or data roles at leading AI or technology companies

  • Proven ability to own complex, multi-stakeholder workflows end-to-end — from scoping and planning through execution, quality assurance, and iteration

  • Working proficiency in Python or SQL, with the ability to query data, automate workflows, or audit annotation outputs; broader familiarity with relational databases or data annotation tooling equally valued

  • Experience designing or optimising data operations processes with a strong eye for quality, consistency, and scalability — ideally in a context involving human-in-the-loop workflows or structured labelling tasks

  • Demonstrated ability to engage effectively with both technical stakeholders (ML engineers, data scientists) and non-technical clients, translating requirements clearly in both directions

  • Bonus: hands-on experience with computer vision, generative AI, or multimodal data workflows; prior exposure to data annotation platforms or quality management frameworks; experience coaching or managing operational teams

Why Encord

  • Competitive salary, commission, and meaningful equity in a high-growth start-up

  • Clear, accelerated growth opportunities as the company scales rapidly

  • Strong in-person culture: 4 days/week

  • Flexible PTO to fully recharge

  • Annual learning & development budget

  • Comprehensive health, dental, and vision coverage

  • Frequent travel opportunities across the U.S., London, and Europe

  • Bi-annual company offsites, twice-weekly team lunches, and monthly socials