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Machine Learning Data Annotation Jobs (NOW HIRING)

NEURA Robotics

Your mission & challenges As an AI Data Annotation Specialist, you will operate at the intersection of data ingestion, processing, and machine learning. Your primary responsibility is to design and ...

JPMorgan Chase & Co.

Manage and coach a team of Machine Learning Data Domain analysts to support data annotation and label data/content using annotation tools and analysis * Partner with leads in Data Science ...

New

Innodata Inc.

Data & Annotation Engineer

Washington, DC · On-site

$55 - $60/hr

Bachelor's degree in Computer Science, Machine Learning, Data Science, or related field required ... CVAT annotation platform - AI feature configuration and operation * DoD or IC data program ...

Welocalize, Inc.

Data Quality Analyst

Modify and refine machine learning data creation, annotation, and rating guidelines. Model Training and Evaluation: * Initiate model training processes using internal tools and command-line ...

TEKsystems

Data Annotation Specialist

Austin, TX · Remote

$25 - $35/hr

Familiarity with AI and machine learning concepts. Additional language skills, which are beneficial for multilingual data annotation projects. Proven track record of handling confidential and ...

Welocalize, Inc.

Data Labeling Associate

New York, NY

$17.50 - $22.75/hr

Modify and refine machine learning data creation, annotation, and rating guidelines. Model Training and Evaluation: * Initiate model training processes using internal tools and command-line ...

Welocalize, Inc.

Data Labeling Associate

San Diego, CA

$17 - $22/hr

Modify and refine machine learning data creation, annotation, and rating guidelines. Model Training and Evaluation: * Initiate model training processes using internal tools and command-line ...

Welocalize, Inc.

Data Labeling Associate

$16.50 - $21.25/hr

Modify and refine machine learning data creation, annotation, and rating guidelines. Model Training and Evaluation: * Initiate model training processes using internal tools and command-line ...

Welocalize, Inc.

Data Quality Analyst

New York, NY · On-site

Modify and refine machine learning data creation, annotation, and rating guidelines. Model Training and Evaluation: * Initiate model training processes using internal tools and command-line ...

Welocalize, Inc.

Data Labeling Associate

San Diego, CA · On-site

$17 - $22/hr

Modify and refine machine learning data creation, annotation, and rating guidelines. Model Training and Evaluation: * Initiate model training processes using internal tools and command-line ...

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Machine Learning Data Annotation information

What is machine learning data annotation?

Machine learning data annotation is the process of labeling or tagging data—such as images, text, audio, or video—so that it can be used to train machine learning models. Annotators add relevant information to raw data, helping algorithms learn to recognize patterns and make predictions. This process is essential for supervised learning, as models require accurately labeled datasets to achieve high performance. Data annotation can be done manually or with the help of specialized tools, and is a critical step in developing reliable AI systems.

What are the key skills and qualifications needed to thrive as a machine learning data annotation specialist?

To thrive as a Machine Learning Data Annotation Specialist, you need strong attention to detail, familiarity with data labeling processes, and a basic understanding of machine learning concepts, often supported by a relevant degree or specialized training. Experience with annotation platforms such as Labelbox, Supervisely, or CVAT, and knowledge of data management systems are commonly required. Diligence, consistency, and effective communication are essential soft skills for ensuring high-quality annotated datasets and collaborating with machine learning teams. These skills are crucial for producing accurate training data, which directly impacts the performance and reliability of AI models.

What are some common challenges faced in a machine learning data annotation role, and how can they be addressed?

One common challenge in a Machine Learning Data Annotation role is maintaining high consistency and accuracy, especially when dealing with large volumes of complex data. Ambiguities in labeling guidelines or unclear data points can also make the work more difficult. To address these issues, annotators often participate in regular training sessions, utilize detailed instruction manuals, and collaborate closely with quality assurance teams. Open communication with project managers and peers is also essential to clarify uncertainties and ensure alignment with project standards.

What is the difference between Machine Learning Data Annotation vs Data Labeler?

AspectMachine Learning Data AnnotationData Labeler
CredentialsBasic computer skills, attention to detailBasic computer skills, attention to detail
Work EnvironmentRemote or on-site, tech companies, AI projectsRemote or on-site, data processing centers, AI companies
Industry UsageAI, machine learning, data scienceData management, AI, machine learning
Job FocusCreating labeled datasets for training AI modelsLabeling data to assist AI training

Machine Learning Data Annotation involves creating detailed labels and annotations for datasets used to train AI models, often requiring understanding of specific data types. Data Labelers focus on applying labels to data, typically with less emphasis on complex annotations. Both roles are essential in AI development, but data annotation often involves more specialized tasks and tools.

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Machine Learning Data Annotation jobs near you
Infographic showing various Machine Learning Data Annotation job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 84% Full Time, 12% Part Time, and 2% Contract. Highlights an 85% Physical, 3% Hybrid, and 12% Remote job distribution.

ML Systems Engineer, Data Labeling Engineering - Early Career

Sunnyvale, CA • On-site

Jobtailor
See more jobs at Jobtailorexternal

$136K - $163K/yr

Other

Posted 2 days ago

New

Job description

  • Develop automation and tooling for labeling workflows and data quality, including efficiency dashboards, automated quality assurance, and autolabel review tools
  • Collaborate with ML engineers to design and integrate ML-driven data annotation, including pre-labeling, autolabeling, and active learning loops
  • Help evolve labeling workflows from human-only processes toward machine-led labeling at scale
  • Design, implement, and test scalable, high-performance user experiences and services using modern full-stack and/or frontend technologies
  • Ship features across multiple product surfaces to improve the speed and accuracy of data labeling for new models and cities
  • Apply production engineering practices including code review, automated testing, observability, CI/CD, and incremental delivery
  • Use AI-assisted development workflows such as code assistants, automated documentation, test generation, and operational triage while maintaining code and product quality
  • Partner with labelers, ML engineers, Product Operations, Product Management, Data Science, and other cross-functional teams to improve the platform
Requirements
  • Recently completed a bachelor’s, master’s, or PhD degree in Computer Science, Computer Engineering, Software Engineering, Artificial Intelligence, Machine Learning, or a related STEM field; completed degree must have been awarded within the past 9 months
  • Experience building software through coursework, internships, research, personal projects, or prior professional experience
  • Programming experience in one or more of Python, TypeScript, JavaScript, Go, Java, or C++
  • Familiarity with object-oriented design, design patterns, data structures, algorithms, API/interface design, and engineering best practices
  • Exposure to building applications, services, data pipelines, or user-facing tools in a collaborative environment
  • Ability to learn new technologies, reason about technical tradeoffs, and communicate clearly with engineering and cross-functional partners
  • Interest in autonomous vehicles, robotics, machine learning, data-centric AI, or developer and ML platform technologies
  • Preferred: graduation between December 2025 and August 2026, with availability to begin employment in 2026
  • Preferred experience with Python, TypeScript, Go, React, SQL, Redux, gRPC, GraphQL, WebGL, or similar tools
  • Preferred familiarity with scalable software system design, data modeling, API/interface design, observability, CI/CD, or test-driven development
  • Preferred experience with computer vision, machine learning, data-centric AI, data annotation, data quality, or autolabeling workflows
  • Preferred familiarity with data labeling or annotation platforms, annotation user interfaces, workflow engines, or quality systems
  • Preferred experience with A/B testing, telemetry, observability systems, data-intensive applications, visualization-heavy applications, AI-assisted engineering workflows, and cross-functional collaboration
Core Competencies

Demonstrates expertise in developing automation and tooling for data labeling workflows, with strong programming skills in Python, TypeScript, and JavaScript. Proficient in applying production engineering practices and collaborating with cross-functional teams to enhance data quality and user experiences.

Highest-signal resume keywords
  • Python Programming
  • TypeScript Programming
  • Data Annotation Workflows
  • CI/CD Practices
  • Machine Learning Integration
Hard Skills
  • JavaScript Programming
  • Go Programming
  • Java Programming
  • C++ Programming
  • Object-Oriented Design
  • Data Structures
  • Algorithms
  • API Design
  • Test-Driven Development
  • Data Modeling
Soft Skills
  • Clear Communication
  • Collaborative Problem Solving
  • Technical Tradeoff Reasoning
  • Adaptability to New Technologies
Industry Keywords
  • Data-Centric AI
  • Autonomous Vehicles
  • Robotics
  • Data Quality
  • Machine Learning
  • Data Annotation
  • Quality Systems
  • Workflow Engines
  • Cross-Functional Collaboration
  • AI-Assisted Engineering
Tools & Technologies
  • React
  • SQL
  • Redux
  • GRPC
  • GraphQL
  • WebGL
  • Observability Systems
  • Telemetry
  • A/B Testing
  • Visualization Tools
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