Track data collection and annotation budget. 3. Annotation & Labeling Oversight * Coordinate data annotation activities with internal teams and external vendors. * Track annotation progress ...
Track data collection and annotation budget. 3. Annotation & Labeling Oversight * Coordinate data annotation activities with internal teams and external vendors. * Track annotation progress ...
Track data collection and annotation budget. 3. Annotation & Labeling Oversight * Coordinate data annotation activities with internal teams and external vendors. * Track annotation progress ...
Track data collection and annotation budget. 3. Annotation & Labeling Oversight * Coordinate data annotation activities with internal teams and external vendors. * Track annotation progress ...
Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate in remote assignments or attend on-site sessions when required * Follow project guidelines and ...
Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate in remote assignments or attend on-site sessions when required * Follow project guidelines and ...
Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate in remote assignments or attend on-site sessions when required * Follow project guidelines and ...
Quick apply
Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate in remote assignments or attend on-site sessions when required * Follow project guidelines and ...
Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate in remote assignments or attend on-site sessions when required * Follow project guidelines and ...
Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate in remote assignments or attend on-site sessions when required * Follow project guidelines and ...
Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate in remote assignments or attend on-site sessions when required * Follow project guidelines and ...
Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate in remote assignments or attend on-site sessions when required * Follow project guidelines and ...
Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate in remote assignments or attend on-site sessions when required * Follow project guidelines and ...
Quick apply
Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate in remote assignments or attend on-site sessions when required * Follow project guidelines and ...
Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate in remote assignments or attend on-site sessions when required * Follow project guidelines and ...
Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate in remote assignments or attend on-site sessions when required * Follow project guidelines and ...
Annotation Labelling information
What are the key skills and qualifications needed to thrive as an Annotation Labelling Specialist, and why are they important?
To thrive as an Annotation Labelling Specialist, you need strong attention to detail, data analysis capabilities, and familiarity with data annotation standards, usually supported by a background in computer science or related fields. Proficiency with annotation tools such as Labelbox, CVAT, or Supervisely, and sometimes knowledge of basic programming or scripting, is typically required. Excellent communication, consistency, and the ability to follow complex instructions are crucial soft skills for producing high-quality labeled data. These skills ensure the accuracy and reliability of datasets, which are foundational for successful machine learning and AI model development.
What are some common challenges faced by Annotation Labelling professionals, and how can they be managed?
Annotation Labelling professionals often encounter challenges such as maintaining high accuracy while handling repetitive data, meeting tight deadlines, and adapting to evolving project guidelines. To manage these, it’s important to develop strong attention to detail, regularly communicate with team leads to clarify instructions, and leverage annotation tools efficiently. Collaborating closely with quality assurance teams can also help identify and correct errors early, ensuring consistently high-quality outputs.
What is annotation labelling?
Annotation labelling is the process of tagging or marking data—such as images, text, or audio—with relevant information or labels. This is an essential step in preparing datasets for machine learning and artificial intelligence models, as it helps algorithms understand and learn from raw data. Annotation labelling can include tasks like identifying objects in photos, transcribing speech, or categorizing text. Skilled annotators ensure accuracy and consistency to improve model performance. People in this role often use specialized tools or software to streamline and standardize the annotation process.
What is the difference between Annotation Labelling vs Data Labeling Specialist?
| Aspect | Annotation Labelling | Data Labeling Specialist |
|---|---|---|
| Credentials | Basic technical skills, attention to detail | Similar skills, sometimes additional domain knowledge |
| Work Environment | Data annotation platforms, remote or office | Data annotation tasks, often remote or in-office |
| Industry Usage | AI, machine learning, autonomous vehicles | AI, machine learning, healthcare, retail |
| Search & Comparison | Commonly compared for entry-level data tasks | Related but broader role |
Annotation Labelling involves marking data such as images, text, or videos to train AI models. Data Labeling Specialists perform similar tasks but may have a broader scope, including verifying and managing labeled data. Both roles are essential in AI development, often overlapping in skills and work environment, but Annotation Labelling is more focused on the annotation process itself.
What are popular job titles related to Annotation Labelling jobs in Wisconsin? For Annotation Labelling jobs in Wisconsin, the most frequently searched job titles are:
What job categories do people searching Annotation Labelling jobs in Wisconsin look for? The top searched job categories for Annotation Labelling jobs in Wisconsin are:
What cities in Wisconsin are hiring for Annotation Labelling jobs? Cities in Wisconsin with the most Annotation Labelling job openings:
GE HealthCare rating
8.5
Based on 130 frontline employees who took The Breakroom Quiz
61st of 415 rated machine equipment manufacturers
Job description
Job Description Summary
The Data Manager - AI Development is a key role within the AI Development team responsible for planning, coordinating, tracking, and governing data used to develop AI enabled medical device features. This role works closely with AI/ML engineers to define data needs for AI features, coordinates with internal and external data collection teams/clinical team, oversees annotation activities, and ensures data readiness, traceability, and compliance throughout the AI development lifecycle.
The role is execution focused and coordination driven, ensuring that the right data is available, prepared, and documented at the right time to support AI feature development, evaluation, and regulatory readiness. Strong planning, communication, and organizational skills are essential for success in this role.
Please note - this is a full time, onsite role located in Waukesha, WI.
Job Description
Roles and Responsibilities
Key Roles and Responsibilities
1. AI Data Planning & Requirements
2. Data Collection Coordination
3. Annotation & Labeling Oversight
4. Data Governance & Compliance Support
5. Program Tracking & Communication
Required Qualifications
Desired Characteristics
Why Join Us?
We will not sponsor individuals for employment visas, now or in the future, for this job opening.
Additional Information
GE HealthCare offers a great work environment, professional development, challenging careers, and competitive compensation. GE HealthCare is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other characteristics protected by law.
GE HealthCare will only employ those who are legally authorized to work in the United States for this opening. Any offer of employment is conditioned upon the successful completion of a drug screen (as applicable).
While GE HealthCare does not currently require U.S. employees to be vaccinated against COVID-19, some GE HealthCare customers have vaccination mandates that may apply to certain GE HealthCare employees.
Relocation Assistance Provided: No
The Data Manager - AI Development is a key role within the AI Development team responsible for planning, coordinating, tracking, and governing data used to develop AI enabled medical device features. This role works closely with AI/ML engineers to define data needs for AI features, coordinates with internal and external data collection teams/clinical team, oversees annotation activities, and ensures data readiness, traceability, and compliance throughout the AI development lifecycle.
The role is execution focused and coordination driven, ensuring that the right data is available, prepared, and documented at the right time to support AI feature development, evaluation, and regulatory readiness. Strong planning, communication, and organizational skills are essential for success in this role.
Please note - this is a full time, onsite role located in Waukesha, WI.
Job Description
Roles and Responsibilities
Key Roles and Responsibilities
1. AI Data Planning & Requirements
- Partner with AI/ML engineers and technical leads to define data requirements for AI features, including dataset scope, diversity, and usage intent.
- Translate feature and model needs into clear data requirements that guide collection, annotation, and preparation activities.
- Support creation and maintenance of AI data planning artifacts aligned with internal Quality Management System (QMS) requirements.
2. Data Collection Coordination
- Coordinate with centralized and distributed data collection teams to support AI development needs.
- Track data sourcing activities across multiple programs and stakeholders.
- Maintain data collection dashboards that provide visibility into status, coverage, risks, and gaps.
- Track data collection and annotation budget.
3. Annotation & Labeling Oversight
- Coordinate data annotation activities with internal teams and external vendors.
- Track annotation progress, throughput, and quality metrics.
- Maintain annotation dashboards to ensure timely delivery aligned with AI development milestones.
4. Data Governance & Compliance Support
- Support execution of AI data management practices including:
- Data control planning
- Data segregation between training, holdout, and testing datasets
- Data preparation and inclusion criteria
- Data traceability and usage documentation
- Ensure datasets are properly documented and traceable to their original sources to support audits and regulatory submissions.
- Act as a point of coordination to ensure data activities align with applicable QMS work instructions for AI development.
5. Program Tracking & Communication
- Serve as the central coordination point for AI data activities across engineering, data operations, and program teams.
- Proactively communicate status, risks, and dependencies to stakeholders.
- Support planning reviews, design reviews, and readiness discussions with accurate data status reporting.
Required Qualifications
- Bachelor's degree in Engineering, Computer Science, Data Science, Biomedical Engineering, or a related technical discipline with 4 years of experience.
- Experience in data management, data operations, or program coordination roles supporting technical or engineering teams.
- Demonstrated ability to plan, track, and coordinate complex workflows across multiple stakeholders.
- Strong written and verbal communication skills, with the ability to translate technical needs into actionable plans.
- Experience creating and maintaining dashboards (eg. PowerBI, excel, smartsheet) trackers, or reports for operational visibility.
- Familiarity with structured data workflows(eg. SQL), including data collection, annotation, and dataset organization(eg. Python).
- Ability to work effectively in cross-functional teams within a regulated or quality-driven environment.
Desired Characteristics
- Experience supporting AI / machine learning development teams, particularly in healthcare or medical devices.
- Familiarity with AI data lifecycle concepts, including training, validation, and testing datasets.
- Knowledge of medical imaging data formats and annotation tools (e.g., V7).
- Exposure to regulated development environments (medical devices, healthcare software, or similar).
- Understanding of data governance concepts such as data traceability, segregation, and controlled usage.
- Experience coordinating external vendors or annotation partners.
- Comfort working with ambiguity and evolving requirements in early-stage AI feature development.
- Experience with Microsoft Forms
Why Join Us?
- Be at the forefront of AI-driven healthcare innovation.
- Collaborate with a multidisciplinary team passionate about improving patient outcomes.
- Shape the future of regulatory processes for cutting-edge medical technologies.
We will not sponsor individuals for employment visas, now or in the future, for this job opening.
Additional Information
GE HealthCare offers a great work environment, professional development, challenging careers, and competitive compensation. GE HealthCare is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other characteristics protected by law.
GE HealthCare will only employ those who are legally authorized to work in the United States for this opening. Any offer of employment is conditioned upon the successful completion of a drug screen (as applicable).
While GE HealthCare does not currently require U.S. employees to be vaccinated against COVID-19, some GE HealthCare customers have vaccination mandates that may apply to certain GE HealthCare employees.
Relocation Assistance Provided: No
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