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Machine Learning Data Annotation Jobs (NOW HIRING)

Trimble

Machine Learning Data Scientist

Westminster, CO · On-site

$122.64K - $165.47K/yr

Essential Skills & Experience * 5+ years of expertise in data science or engineering, specifically building and deploying predictive machine learning models. * Proficiency in Python and SQL for data ...

Apple

Machine Learning - Data Scientist

Sunnyvale, CA · On-site

The Video Engineering Data Analytics and Quality group is seeking an expert in evaluating machine learning and deep learning models, including foundation models and multimodal systems. ..This role ...

Mercor

Data Scientist | Upto $100/hr

New York, NY · Remote

$100/hr

... experience in Machine Learning , Data Science , Software Engineering , Computer Science ... Prior experience with data annotation, labeling, evaluation, or human feedback collection.

Apple

Machine Learning - Data Scientist

Sunnyvale, CA

$147.40K - $272.10K/yr

The Video Engineering Data Analytics and Quality group is seeking an expert in evaluating machine learning and deep learning models, including foundation models and multimodal systems. This role will ...

Mercor

Data Science Expert - Fully Remote

New York, NY · Remote

$70 - $100/hr

... experience in Machine Learning , Data Science , Software Engineering , Computer Science ... Prior experience with data annotation, labeling, evaluation, or human feedback collection.

Docugami

Machine Learning / Data Scientist

Kirkland, WA · On-site

Docugami is looking for Machine Learning, Data Science and Math PhD researchers to work alongside our world-renowned science and engineering team to create a revolutionary product. We are looking for ...

Rime Labs

Machine Learning Engineer, Data

San Francisco, CA · On-site

$134.90K - $162K/yr

Machine Learning Engineer, Data & Training Infrastructure Rime builds voice AI for enterprises ... End-to-end audio annotation pipeline : Currently some stages exist as prototypes; productionizing ...

Cohere

Data Annotation Specialist, Safety

New York, NY · On-site

Machines (or Large Language Models to be exact) learn in similar ways to humans: by way of feedback ... As a Data Annotation Specialist on safety task, you will: * Evaluate and improve model safety:

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Machine Learning Data Annotation information

What are the key skills and qualifications needed to thrive as a Machine Learning Data Annotation Specialist, and why are they important?

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 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 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 May 2026, with employment types broken down into 67% Full Time, and 33% Part Time. Highlights an 67% In-person, and 33% Remote job distribution.
Generative AI Data Analyst - USA (Remote)

Generative AI Data Analyst - USA (Remote)

Welo Data

Charleston, WV • Remote

$27.38/hr

Full-time

Posted 23 days ago

Job description

OVERVIEW

We are seeking a Generative AI Analyst to support a high-impact machine learning project. This role focuses on creating high-quality prompts and responses across diverse topics and leading labeling initiatives with internal and external partners. The ideal candidate is a strong communicator with native-level U.S. English, experienced in working with data and comfortable training teams on best practices for LLM development. This position is fully remote and suited for someone motivated to work with cutting-edge AI technologies.

Project Details

Job Title: Generative AI Analyst
Location: Remote
Hours: 40 hours weekly
Language: English (US)
Start date: April 2026
Employment Type: Full-time W-2 employee with benefits – 5 days a week
Pay rate: $27.38/hour
If you reside in California, please apply to the California-specific posting for the applicable rate

Must have valid work authorization in the US (Welo Data does not sponsor VISAs at this time).

Key Responsibilities
  • Creatively writing prompts and responses to a variety of diverse topics
  • Perform LLM annotation and evaluation tasks (ranking, scoring, labeling, tagging)
  • Evaluate model outputs for accuracy, relevance, and instruction-following
  • Identify and document issues such as hallucinations and inconsistencies
  • Participating in and/or supporting labeling workflows, including hands-on annotation and collaboration with internal or external teams
  • Training teams on best practices for creating Large Language Models/Data sets
Requirements
  • Hands-on experience performing data annotation or evaluation tasks (e.g., labeling, ranking, scoring, or tagging LLM outputs)
  • Native or near-native English with excellent writing skills
  • Strong attention to detail and ability to follow guidelines consistently
  • Self-driven, motivated and enthusiastic to work on state-of-art machine learning tools
  • 4 year Accredited College degree or equivalent experience

Ways to stand out from the crowd:

  • College Degree or experience in Linguistics, English Literature, Creative Writing, Journalism, and domain knowledge (Law/Medical/Math/Coding/etc.)
  • Experience working in annotation platforms or structured labeling environments is a plus
  • Deep understanding of Large Language Models/RLHF
  • Experience in labeling/tagging of frames/tasks/prompts to prepare for DNN
  • QA/testing experience

Please note that in order to verify work authorization as is required by Federal law (I-9 process), all new employees must complete a live video verification with their selected IDs and provide photos of these selected IDs within their first 3 days of employment.