... annotation management, and feature store integration • Experience with CI/CD pipelines (Jenkins) and infrastructure-as-code tools (Terraform, CloudFormation) for automated deployment and ...
... annotation management, and feature store integration • Experience with CI/CD pipelines (Jenkins) and infrastructure-as-code tools (Terraform, CloudFormation) for automated deployment and ...
Data Services Manager
Tampa, FL · On-site
Oversee data labeling, annotation, and versioning for machine learning datasets. Generative AI Data Management * Establish data management protocols for generative AI applications and large language ...
Data Services Manager
Tampa, FL · On-site
Oversee data labeling, annotation, and versioning for machine learning datasets. Generative AI Data Management * Establish data management protocols for generative AI applications and large language ...
Data Services Manager
Tampa, FL · On-site
Oversee data labeling, annotation, and versioning for machine learning datasets. Generative AI Data Management * Establish data management protocols for generative AI applications and large language ...
Data Services Manager
Tampa, FL · On-site
Oversee data labeling, annotation, and versioning for machine learning datasets. Generative AI Data Management * Establish data management protocols for generative AI applications and large language ...
Data Scientist Lead
Tampa, FL · On-site
Establish best practices for annotation quality management, training data curation, active learning strategies, and ground truth validation to ensure high-quality labeled datasets. • Design, manage ...
Data Scientist Lead
Tampa, FL · On-site
Establish best practices for annotation quality management, training data curation, active learning strategies, and ground truth validation to ensure high-quality labeled datasets. • Design, manage ...
Establish best practices for annotation quality management, training data curation, active learning strategies, and ground truth validation to ensure high-quality labeled datasets. * Design, manage ...
Establish best practices for annotation quality management, training data curation, active learning strategies, and ground truth validation to ensure high-quality labeled datasets. * Design, manage ...
Establish best practices for annotation quality management, training data curation, active learning strategies, and ground truth validation to ensure high-quality labeled datasets. * Design, manage ...
Establish best practices for annotation quality management, training data curation, active learning strategies, and ground truth validation to ensure high-quality labeled datasets. * Design, manage ...
Data Scientist Lead
Tampa, FL · On-site
Establish best practices for annotation quality management, training data curation, active learning strategies, and ground truth validation to ensure high-quality labeled datasets. * Design, manage ...
Data Scientist Lead
Tampa, FL · On-site
Establish best practices for annotation quality management, training data curation, active learning strategies, and ground truth validation to ensure high-quality labeled datasets. * Design, manage ...
AI/ML Lead Data Engineer - Automation/Image Processing
Tampa, FL · On-site
$96.90K - $127.60K/yr
Understanding of data requirements for training deep learning models including dataset preparation, annotation management, and feature store integration * Experience with CI/CD pipelines (Jenkins ...
AI/ML Lead Data Engineer - Automation/Image Processing
Tampa, FL · On-site
$96.90K - $127.60K/yr
Understanding of data requirements for training deep learning models including dataset preparation, annotation management, and feature store integration * Experience with CI/CD pipelines (Jenkins ...
AI/ML Lead Data Engineer - Automation/Image Processing
Tampa, FL · On-site
$96.90K - $127.60K/yr
Understanding of data requirements for training deep learning models including dataset preparation, annotation management, and feature store integration * Experience with CI/CD pipelines (Jenkins ...
AI/ML Lead Data Engineer - Automation/Image Processing
Tampa, FL · On-site
$96.90K - $127.60K/yr
Understanding of data requirements for training deep learning models including dataset preparation, annotation management, and feature store integration * Experience with CI/CD pipelines (Jenkins ...
AI/ML Lead Data Engineer - Automation/Image Processing
$96.90K - $127.60K/yr
Understanding of data requirements for training deep learning models including dataset preparation, annotation management, and feature store integration * Experience with CI/CD pipelines (Jenkins ...
AI/ML Lead Data Engineer - Automation/Image Processing
$96.90K - $127.60K/yr
Understanding of data requirements for training deep learning models including dataset preparation, annotation management, and feature store integration * Experience with CI/CD pipelines (Jenkins ...
Data Engineer III
Melbourne, FL · On-site
... managing metadata and annotation workflows for sensor datasets Ability to obtain and maintain a US ... data pipelines and MLOps workflows Experience deploying real-time or near-real-time streaming ...
Data Engineer III
Melbourne, FL · On-site
... managing metadata and annotation workflows for sensor datasets Ability to obtain and maintain a US ... data pipelines and MLOps workflows Experience deploying real-time or near-real-time streaming ...
Data Scientist
Miami, FL · On-site
Manage, organize, and support analysis of multi-source datasets collected across SEFSC surveys ... Support annotation of datasets and development of machine learning models, including training and ...
Data Scientist
Miami, FL · On-site
Manage, organize, and support analysis of multi-source datasets collected across SEFSC surveys ... Support annotation of datasets and development of machine learning models, including training and ...
Data Scientist
Miami, FL · On-site +1
Manage, organize, and support analysis of multi-source datasets collected across SEFSC surveys ... Support annotation of datasets and development of machine learning models, including training and ...
Data Scientist
Miami, FL · On-site +1
Manage, organize, and support analysis of multi-source datasets collected across SEFSC surveys ... Support annotation of datasets and development of machine learning models, including training and ...
... annotation algorithms, we will better understand the genetic architecture of underserved ... and data collection / management. * Maintain an overview, ensuring availability of inventory ...
... annotation algorithms, we will better understand the genetic architecture of underserved ... and data collection / management. * Maintain an overview, ensuring availability of inventory ...
... annotation algorithms, we will better understand the genetic architecture of underserved ... and data collection / management. * Maintain an overview, ensuring availability of inventory ...
... annotation algorithms, we will better understand the genetic architecture of underserved ... and data collection / management. * Maintain an overview, ensuring availability of inventory ...
... annotation algorithms, we will better understand the genetic architecture of underserved ... and data collection / management. * Maintain an overview, ensuring availability of inventory ...
Quick apply
... annotation algorithms, we will better understand the genetic architecture of underserved ... and data collection / management. * Maintain an overview, ensuring availability of inventory ...
... goals Perform data collection, management, and statistical analysis of imaging and clinical ... annotation, segmentation, and quantitative analysis using advanced imaging tools and software ...
... goals Perform data collection, management, and statistical analysis of imaging and clinical ... annotation, segmentation, and quantitative analysis using advanced imaging tools and software ...
Establish robust MLOps practices and annotation workflows, including model versioning, automated ... design and manage labeling strategies for training data, ensuring high-quality ground truth ...
Establish robust MLOps practices and annotation workflows, including model versioning, automated ... design and manage labeling strategies for training data, ensuring high-quality ground truth ...
CADD Technician
Kissimmee, FL · On-site
Data Organization: Import and organize GPS and total station data while maintaining clean point ... Managers. * Compliance: Precisely follow company templates, layers, and annotation standards.
Quick apply
CADD Technician
Kissimmee, FL · On-site
Data Organization: Import and organize GPS and total station data while maintaining clean point ... Managers. * Compliance: Precisely follow company templates, layers, and annotation standards.
Establish robust MLOps practices and annotation workflows, including model versioning, automated ... design and manage labeling strategies for training data, ensuring high-quality ground truth ...
Establish robust MLOps practices and annotation workflows, including model versioning, automated ... design and manage labeling strategies for training data, ensuring high-quality ground truth ...
Data Annotation Manager information
See Florida salary details
$23.2K - $32.7K
7% of jobs
$32.7K - $42.3K
8% of jobs
$48.8K is the 25th percentile. Wages below this are outliers.
$42.3K - $51.9K
14% of jobs
$51.9K - $61.5K
18% of jobs
The median wage is $63.2K / yr.
$61.5K - $71.1K
15% of jobs
$71.1K - $80.6K
7% of jobs
$87.8K is the 75th percentile. Wages above this are outliers.
$80.6K - $90.2K
7% of jobs
$90.2K - $99.8K
6% of jobs
$99.8K - $109.4K
6% of jobs
$109.4K - $119K
4% of jobs
$119K - $128.5K
6% of jobs
$23.2K
$72.6K
$128.5K
How much do data annotation manager jobs pay per year?
As of Jun 1, 2026, the average yearly pay for data annotation manager in Florida is $72,595.00, according to ZipRecruiter salary data. Most workers in this role earn between $49,300.00 and $93,800.00 per year, depending on experience, location, and employer.
What are the key skills and qualifications needed to thrive as a Data Annotation Manager, and why are they important?
To thrive as a Data Annotation Manager, you need expertise in data labeling processes, quality control, and a solid understanding of machine learning concepts, usually backed by a degree in computer science or a related field. Proficiency with annotation tools such as Labelbox, Supervisely, or CVAT, as well as experience with project management systems, is commonly required. Exceptional leadership, attention to detail, and strong communication skills help manage teams and ensure high annotation accuracy. These skills are critical for delivering reliable labeled datasets, which are essential for building effective AI and machine learning models.
What are some common challenges faced by Data Annotation Managers, and how can they be addressed?
Data Annotation Managers often encounter challenges such as maintaining high annotation quality across large and diverse datasets, managing a distributed team of annotators, and meeting tight project deadlines. To address these, it's important to implement robust quality assurance processes, provide ongoing training for annotators, and establish clear communication channels. Leveraging annotation tools with built-in validation features can also help ensure consistency and accuracy. Building a positive and collaborative team environment further contributes to better outcomes and workflow efficiency.
What does a Data Annotation Manager do?
A Data Annotation Manager oversees the process of labeling and categorizing data used to train machine learning models. They manage teams of annotators, ensure data quality, develop annotation guidelines, and coordinate with data scientists to meet project requirements. Their role is critical in maintaining high standards of accuracy and efficiency, as well as ensuring that datasets are properly prepared for AI and machine learning applications.
What is the difference between Data Annotation Manager vs Data Labeling Specialist?
| Aspect | Data Annotation Manager | Data Labeling Specialist |
|---|---|---|
| Credentials | Bachelor's degree in related field, experience in data management | High school diploma or equivalent, training in labeling tools |
| Work Environment | Team management, project oversight, collaboration with data scientists | Hands-on labeling work, using annotation tools, focused on data tagging |
| Industry Usage | Used in AI/ML projects for overseeing annotation teams | Performs the actual data labeling tasks in machine learning workflows |
The Data Annotation Manager oversees the entire annotation process, managing teams and ensuring quality, while the Data Labeling Specialist focuses on executing labeling tasks. Both roles are essential in AI/ML data preparation but differ in responsibilities and scope.
What are the most commonly searched types of Data Annotation jobs in Florida? The most popular types of Data Annotation jobs in Florida are:
What are popular job titles related to Data Annotation Manager jobs in Florida? For Data Annotation Manager jobs in Florida, the most frequently searched job titles are:
What job categories do people searching Data Annotation Manager jobs in Florida look for? The top searched job categories for Data Annotation Manager jobs in Florida are:
What cities in Florida are hiring for Data Annotation Manager jobs? Cities in Florida with the most Data Annotation Manager job openings:
Full-time
Posted 25 days ago
Job description
Job Summary:
JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers and businesses. As a Lead Data Engineer within the Commercial & Investment Bank, you will design and maintain data pipelines for processing large volumes of scanned document images, ensuring data quality and compliance with regulatory standards.
Responsibilities:
• Design, build, and maintain scalable, high-performance data pipelines and infrastructure to support ingestion, processing, and storage of large volumes of scanned document images across enterprise-wide workflows
• Architect end-to-end data solutions on AWS cloud services to enable seamless flow of scanned images from source systems through OCR processing, model inference, and downstream data extraction and categorization pipelines
• Develop robust image preprocessing and OCR integration pipelines that handle TIF/PNG format conversion, normalization, resolution enhancement, noise reduction, and batching to prepare scanned documents for downstream computer vision and OCR models
• Build and optimize data pipelines that integrate OCR engine outputs, extracting structured text and metadata from scanned images and routing them into databases and analytics platforms for further processing
• Design and manage data storage architectures and containerized deployments, using Oracle databases and AWS-native stores (S3, EFS) to efficiently catalog, index, and retrieve extracted text, classification labels, and metadata from processed document images
• Drive the adoption of containerized deployment strategies using AWS EKS (Elastic Kubernetes Service) to deploy and scale image processing microservices, OCR engines, and data pipeline components with high availability and fault tolerance
• Collaborate closely with data scientists and ML engineers to ensure training datasets for different models, and other computer vision models are properly curated, versioned, labeled, and accessible through well-structured data pipelines
• Evaluate and integrate emerging data technologies and tools to continuously improve pipeline throughput, reduce processing latency for high-volume document scanning workloads, and optimize cost efficiency
• Establish and enforce data quality, lineage, governance, and security frameworks to ensure traceability and integrity of extracted data from scanned documents throughout the entire processing lifecycle
• Partner with security and compliance teams to ensure that scanned document data, extracted PII/PHI, and sensitive content are handled in accordance with regulatory requirements, encryption standards, and access controls
• Lead and mentor a team of data engineers, establishing coding standards, peer review processes, CI/CD workflows, and best practices for building production-grade image and document processing pipelines
Qualifications:
Required:
• Formal training or certification on Data Engineering concepts and 5+ years applied experience
• Strong proficiency in Java, Groovy, and Python for building data pipelines, image preprocessing workflows, automation scripts, and backend data services
• Hands-on experience with image file handling, particularly TIF/PNG format processing, multi-page document splitting, format conversion, and integration with OCR and computer vision pipelines
• Deep hands-on experience with AWS cloud services including S3 (for image storage), Lambda, Step Functions, and CloudWatch for building and monitoring scalable data workflows
• Expertise in AWS EKS (Elastic Kubernetes Service) for deploying and managing containerized image processing, OCR, and data pipeline services using Docker and Kubernetes
• Advanced knowledge of Oracle databases including PL/SQL, performance tuning, partitioning strategies, and data modeling for storing and querying large volumes of extracted document data and classification results
• Familiarity with OCR technologies and the ability to build data pipelines that consume and structure OCR output for downstream analytics and model training
• Understanding of data requirements for training deep learning models including dataset preparation, annotation management, and feature store integration
• Experience with CI/CD pipelines (Jenkins) and infrastructure-as-code tools (Terraform, CloudFormation) for automated deployment and environment management
• Strong understanding of data governance, data quality frameworks, metadata management, and data cataloging, particularly in the context of document-centric and image-heavy data ecosystems
• Excellent leadership, communication, and stakeholder management skills with the ability to drive technical decisions across cross-functional teams
Preferred:
• Domain expertise in the healthcare industry
Company:
With a history tracing its roots to 1799 in New York City, JPMorganChase is one of the world's oldest, largest, and best-known financial institutions—carrying forth the innovative spirit of our heritage firms in global operations across 100 markets. Founded in 2000, the company is headquartered in New York, USA, with a team of 10001+ employees. The company is currently Late Stage.
JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers and businesses. As a Lead Data Engineer within the Commercial & Investment Bank, you will design and maintain data pipelines for processing large volumes of scanned document images, ensuring data quality and compliance with regulatory standards.
Responsibilities:
• Design, build, and maintain scalable, high-performance data pipelines and infrastructure to support ingestion, processing, and storage of large volumes of scanned document images across enterprise-wide workflows
• Architect end-to-end data solutions on AWS cloud services to enable seamless flow of scanned images from source systems through OCR processing, model inference, and downstream data extraction and categorization pipelines
• Develop robust image preprocessing and OCR integration pipelines that handle TIF/PNG format conversion, normalization, resolution enhancement, noise reduction, and batching to prepare scanned documents for downstream computer vision and OCR models
• Build and optimize data pipelines that integrate OCR engine outputs, extracting structured text and metadata from scanned images and routing them into databases and analytics platforms for further processing
• Design and manage data storage architectures and containerized deployments, using Oracle databases and AWS-native stores (S3, EFS) to efficiently catalog, index, and retrieve extracted text, classification labels, and metadata from processed document images
• Drive the adoption of containerized deployment strategies using AWS EKS (Elastic Kubernetes Service) to deploy and scale image processing microservices, OCR engines, and data pipeline components with high availability and fault tolerance
• Collaborate closely with data scientists and ML engineers to ensure training datasets for different models, and other computer vision models are properly curated, versioned, labeled, and accessible through well-structured data pipelines
• Evaluate and integrate emerging data technologies and tools to continuously improve pipeline throughput, reduce processing latency for high-volume document scanning workloads, and optimize cost efficiency
• Establish and enforce data quality, lineage, governance, and security frameworks to ensure traceability and integrity of extracted data from scanned documents throughout the entire processing lifecycle
• Partner with security and compliance teams to ensure that scanned document data, extracted PII/PHI, and sensitive content are handled in accordance with regulatory requirements, encryption standards, and access controls
• Lead and mentor a team of data engineers, establishing coding standards, peer review processes, CI/CD workflows, and best practices for building production-grade image and document processing pipelines
Qualifications:
Required:
• Formal training or certification on Data Engineering concepts and 5+ years applied experience
• Strong proficiency in Java, Groovy, and Python for building data pipelines, image preprocessing workflows, automation scripts, and backend data services
• Hands-on experience with image file handling, particularly TIF/PNG format processing, multi-page document splitting, format conversion, and integration with OCR and computer vision pipelines
• Deep hands-on experience with AWS cloud services including S3 (for image storage), Lambda, Step Functions, and CloudWatch for building and monitoring scalable data workflows
• Expertise in AWS EKS (Elastic Kubernetes Service) for deploying and managing containerized image processing, OCR, and data pipeline services using Docker and Kubernetes
• Advanced knowledge of Oracle databases including PL/SQL, performance tuning, partitioning strategies, and data modeling for storing and querying large volumes of extracted document data and classification results
• Familiarity with OCR technologies and the ability to build data pipelines that consume and structure OCR output for downstream analytics and model training
• Understanding of data requirements for training deep learning models including dataset preparation, annotation management, and feature store integration
• Experience with CI/CD pipelines (Jenkins) and infrastructure-as-code tools (Terraform, CloudFormation) for automated deployment and environment management
• Strong understanding of data governance, data quality frameworks, metadata management, and data cataloging, particularly in the context of document-centric and image-heavy data ecosystems
• Excellent leadership, communication, and stakeholder management skills with the ability to drive technical decisions across cross-functional teams
Preferred:
• Domain expertise in the healthcare industry
Company:
With a history tracing its roots to 1799 in New York City, JPMorganChase is one of the world's oldest, largest, and best-known financial institutions—carrying forth the innovative spirit of our heritage firms in global operations across 100 markets. Founded in 2000, the company is headquartered in New York, USA, with a team of 10001+ employees. The company is currently Late Stage.