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No Experience Machine Learning Data Annotation Jobs

Welocalize, Inc.

Data Quality Analyst

San Francisco, CA · On-site

The ideal candidate will have a foundational understanding of machine learning, data annotation ... Experience: * Ability to work in a fast-paced, collaborative environment. * Excellent communication ...

Welocalize, Inc.

Data Labeling Associate

New York, NY · On-site

$17.50 - $22.75/hr

The ideal candidate will have a foundational understanding of machine learning, data annotation ... Experience: * Ability to work in a fast-paced, collaborative environment. * Excellent communication ...

Welocalize, Inc.

Data Labeling Associate

San Diego, CA · On-site

$17 - $22/hr

The ideal candidate will have a foundational understanding of machine learning, data annotation ... Experience: * Ability to work in a fast-paced, collaborative environment. * Excellent communication ...

Welocalize, Inc.

Data Labeling Associate

$16.50 - $21.25/hr

The ideal candidate will have a foundational understanding of machine learning, data annotation ... Experience: * Ability to work in a fast-paced, collaborative environment. * Excellent communication ...

Welocalize, Inc.

Data Quality Analyst

New York, NY · On-site

The ideal candidate will have a foundational understanding of machine learning, data annotation ... Experience: * Ability to work in a fast-paced, collaborative environment. * Excellent communication ...

Welocalize, Inc.

Data Labeling Associate

New York, NY

$17.50 - $22.75/hr

The ideal candidate will have a foundational understanding of machine learning, data annotation ... Experience: * Ability to work in a fast-paced, collaborative environment. * Excellent communication ...

Q Analysts

Data Annotation Technician

Kirkland, WA · On-site

... AI) and machine learning (ML). Q Analysts is headquartered in San Jose, CA with a presence ... Experience in a related role * Experience in a role requiring usage of a variety of technology ...

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How much do no experience machine learning data annotation jobs pay per year?

As of Sep 15, 2026, the average yearly pay for no experience machine learning data annotation in the United States is $122,738.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $136,000.00 per year, depending on experience, location, and employer.

What is a no experience machine learning data annotation job?

'No Experience Machine Learning Data Annotation' jobs are entry-level positions where individuals help label and categorize data used to train machine learning models. These roles do not require prior experience in data science or programming, making them accessible to beginners. Typical tasks may include tagging images, transcribing audio, or identifying objects in videos. These jobs are essential for improving the accuracy of AI systems and are often done remotely or on a flexible schedule.

What should I expect when collaborating with machine learning engineers as a data annotator with no prior experience?

As a data annotator working alongside machine learning engineers, you will play a vital role in preparing high-quality labeled data for model training. Engineers often provide clear guidelines and feedback on how to label or categorize data accurately, and they may hold regular check-ins to address questions and ensure consistency. While you may not need technical expertise, strong communication and attention to detail are essential, as your work directly impacts the performance of machine learning models. Over time, you’ll become familiar with annotation tools and may have the opportunity to take on more advanced tasks or quality assurance responsibilities.

What are the key skills and qualifications needed to thrive as a no experience machine learning data annotation specialist, and why are they important?

To succeed in a No Experience Machine Learning Data Annotation role, you need strong attention to detail, basic computer literacy, and the ability to follow precise instructions, often requiring at least a high school diploma. Familiarity with data labeling tools (like Labelbox or Supervisely) and experience with spreadsheet software are typically helpful, though many positions offer on-the-job training. Reliability, patience, and effective communication are valuable soft skills for maintaining quality and meeting deadlines. These skills ensure accurate, consistent data labeling, which is critical for training reliable machine learning models.

What is the difference between No Experience Machine Learning Data Annotation vs Data Labeling Specialist?

AspectNo Experience Machine Learning Data AnnotationData Labeling Specialist
Required CredentialsNo formal experience needed, training providedTypically similar, may require basic technical skills
Work EnvironmentRemote or office-based, repetitive tasksRemote or onsite, focused on data preparation
Industry UsageCommon in AI/ML companies, tech startupsUsed across tech, automotive, healthcare sectors
Search & Comparison IntentOften searched by beginners or entry-level job seekersCompared for skill requirements and job scope

Both roles involve labeling data for machine learning models, with minimal experience required. Data Labeling Specialists may have slightly more specialized tasks, but both are entry-level positions vital for AI development.

More about No Experience Machine Learning Data Annotation jobs

What cities are hiring for No Experience Machine Learning Data Annotation jobs?

Cities with the most No Experience Machine Learning Data Annotation job openings:

What are the most commonly searched types of Machine Learning Data Annotation jobs?

The most popular types of Machine Learning Data Annotation jobs are:

What states have the most No Experience Machine Learning Data Annotation jobs?

States with the most job openings for No Experience Machine Learning Data Annotation jobs include:

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For No Experience Machine Learning Data Annotation jobs, the most frequently searched job titles are:

Infographic showing various No Experience Machine Learning Data Annotation job openings in the United States as of September 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $122,738 per year, or $59 per hour.

ML Systems Engineer, Data Labeling Engineering - Early Career

Sunnyvale, CA • On-site

Jobtailor
See more jobs at Jobtailorexternal

$136K - $163K/yr

Other

Posted 3 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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