... data labeling pipelines, annotation workflows, or active learning systems • Understanding of production model serving, including API integration, latency optimization, and monitoring for model ...
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... data labeling pipelines, annotation workflows, or active learning systems • Understanding of production model serving, including API integration, latency optimization, and monitoring for model ...
... data labeling pipelines, annotation workflows, or active learning systems • Understanding of production model serving, including API integration, latency optimization, and monitoring for model ...
... data labeling pipelines, annotation workflows, or active learning systems • Understanding of production model serving, including API integration, latency optimization, and monitoring for model ...
... data labeling pipelines, annotation workflows, or active learning systems • Understanding of production model serving, including API integration, latency optimization, and monitoring for model ...
... data labeling pipelines, annotation workflows, or active learning systems • Understanding of production model serving, including API integration, latency optimization, and monitoring for model ...
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 Nevada? For Annotation Labelling jobs in Nevada, the most frequently searched job titles are:
What job categories do people searching Annotation Labelling jobs in Nevada look for? The top searched job categories for Annotation Labelling jobs in Nevada are:
What cities in Nevada are hiring for Annotation Labelling jobs? Cities in Nevada with the most Annotation Labelling job openings:
AI Engineer -- Classifiers, Media Intelligence & Voice R&D
KORE1 TechnologiesLas Vegas, NV • On-site, Remote
Full-time
Posted 18 days ago
Job description
KORE1, a nationwide provider of staffing and recruiting solutions, has an immediate opening for an AI Engineer — Classifiers, Media Intelligence & Voice R&D that is fully remote.
Overview
This role focuses on building intelligent systems that power large-scale media understanding and organization. You will design, train, and deploy machine learning models that classify, tag, and structure complex datasets, while also contributing to research and development in emerging areas such as voice, audio, and image intelligence.
You'll play a key role in transforming unstructured media into meaningful, searchable, and scalable data, while helping shape the next generation of AI-driven product capabilities.
What You'll Do
• Design and deploy classification models to support content understanding, including style detection, quality scoring, filtering, moderation, and semantic categorization
• Build automated tagging and organization systems that enable efficient media management, search, and discovery
• Develop and maintain training data pipelines, including dataset curation, annotation workflows, and active learning loops
• Research and prototype advanced image intelligence capabilities such as pose estimation, visual similarity, and feature extraction
• Lead experimentation in AI voice and audio technologies, including text-to-speech, voice cloning, and audio synthesis, and help transition research into production-ready systems
• Create evaluation frameworks to measure model performance, accuracy, and drift over time
• Optimize model inference pipelines for performance and cost efficiency through batching, caching, and model optimization techniques
• Integrate ML models into production systems, exposing them through APIs and ensuring reliability at scale
What We're Looking For
• 3+ years of experience building and deploying machine learning models in production environments
• Hands-on experience training models, including dataset preparation, architecture experimentation, hyperparameter tuning, and debugging
• Strong background in computer vision or image classification (e.g., CNNs, vision transformers, CLIP)
• Experience or strong interest in voice and audio AI, such as speech synthesis, voice cloning, or audio classification
• Proficiency in Python and ML frameworks such as PyTorch or TensorFlow
• Experience building or working with data labeling pipelines, annotation workflows, or active learning systems
• Understanding of production model serving, including API integration, latency optimization, and monitoring for model drift
• Familiarity with embedding-based systems, vector search, or semantic similarity techniques
Nice to Have
• Experience with generative models such as diffusion models, GANs, or generative audio systems
• Background in optimizing models using tools like ONNX or TensorRT
• Exposure to research environments or published work in machine learning or AI
Technology Stack
• Python, PyTorch
• GPU-based compute environments
• REST and webhook APIs
• TypeScript (for integration with backend services)
• PostgreSQL (metadata and labeling systems), Redis
• Workflow orchestration tools (e.g., Temporal)
This role focuses on building intelligent systems that power large-scale media understanding and organization. You will design, train, and deploy machine learning models that classify, tag, and structure complex datasets, while also contributing to research and development in emerging areas such as voice, audio, and image intelligence.
You'll play a key role in transforming unstructured media into meaningful, searchable, and scalable data, while helping shape the next generation of AI-driven product capabilities.
What You'll Do
• Design and deploy classification models to support content understanding, including style detection, quality scoring, filtering, moderation, and semantic categorization
• Build automated tagging and organization systems that enable efficient media management, search, and discovery
• Develop and maintain training data pipelines, including dataset curation, annotation workflows, and active learning loops
• Research and prototype advanced image intelligence capabilities such as pose estimation, visual similarity, and feature extraction
• Lead experimentation in AI voice and audio technologies, including text-to-speech, voice cloning, and audio synthesis, and help transition research into production-ready systems
• Create evaluation frameworks to measure model performance, accuracy, and drift over time
• Optimize model inference pipelines for performance and cost efficiency through batching, caching, and model optimization techniques
• Integrate ML models into production systems, exposing them through APIs and ensuring reliability at scale
What We're Looking For
• 3+ years of experience building and deploying machine learning models in production environments
• Hands-on experience training models, including dataset preparation, architecture experimentation, hyperparameter tuning, and debugging
• Strong background in computer vision or image classification (e.g., CNNs, vision transformers, CLIP)
• Experience or strong interest in voice and audio AI, such as speech synthesis, voice cloning, or audio classification
• Proficiency in Python and ML frameworks such as PyTorch or TensorFlow
• Experience building or working with data labeling pipelines, annotation workflows, or active learning systems
• Understanding of production model serving, including API integration, latency optimization, and monitoring for model drift
• Familiarity with embedding-based systems, vector search, or semantic similarity techniques
Nice to Have
• Experience with generative models such as diffusion models, GANs, or generative audio systems
• Background in optimizing models using tools like ONNX or TensorRT
• Exposure to research environments or published work in machine learning or AI
Technology Stack
• Python, PyTorch
• GPU-based compute environments
• REST and webhook APIs
• TypeScript (for integration with backend services)
• PostgreSQL (metadata and labeling systems), Redis
• Workflow orchestration tools (e.g., Temporal)
Compensation depends on experience but is typically 155k - 175k DOE
ABOUT KORE1
Specializing in professional and technical recruiting, KORE1 is committed to supporting top IT, Engineering, Creative, Scientific, Accounting and Finance professionals in their career paths. We build deep relationships with leading companies, connecting them to exceptional talent every day. With extensive industry expertise and unmatched opportunities, our goal is to provide a unique experience for our contractors and consultants as they prepare for their next role. We are passionate about matching the right people with the right companies.
Specializing in professional and technical recruiting, KORE1 is committed to supporting top IT, Engineering, Creative, Scientific, Accounting and Finance professionals in their career paths. We build deep relationships with leading companies, connecting them to exceptional talent every day. With extensive industry expertise and unmatched opportunities, our goal is to provide a unique experience for our contractors and consultants as they prepare for their next role. We are passionate about matching the right people with the right companies.
Kore1 provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability or genetics. In addition to federal law requirements, Kore1 complies with applicable state and local laws governing nondiscrimination in employment in every location in which the company has facilities. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training. Kore1 expressly prohibits any form of workplace harassment based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. Improper interference with the ability of Kore1's employees to perform their job duties may result in discipline up to and including discharge.
About KORE1 Staffing & Recruiting
Sourced by ZipRecruiter
Industry
Recruiting and staffing services
Company size
201 - 500 Employees
Headquarters location
Irvine, CA, US