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Machine Learning Data Annotation Jobs (NOW HIRING)

Welocalize, Inc.

OR

$16 - $20.75/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 Quality Analyst

New York, NY

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.

OR

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

New York, NY · On-site

$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.

OR

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 ...

Mercor

Machine Learning Engineer

New York, NY · Remote

$27 - $32/hr

... machine learning domains. Surface nuances that distinguish expert-level work from surface-level ... Preferred * Prior experience with data annotation, labeling, evaluation, or human feedback ...

Bot Auto

Data Annotation Specialist

Houston, TX · On-site

Collaborate closely with Machine Learning and Mapping engineers to provide feedback on labeling ... Preferred : • Prior experience in data annotation for autonomous driving, robotics, or computer ...

Bot Auto

Data Annotation Specialist

Houston, TX · On-site

They are seeking a meticulous Data Annotation Specialist responsible for creating, refining, and ... Collaborate closely with Machine Learning and Mapping engineers to provide feedback on labeling ...

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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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What are the most commonly searched types of Machine Learning Data Annotation jobs? The most popular types of Machine Learning Data Annotation jobs are:
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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.
Atlas Data Operations Annotation Manager

Atlas Data Operations Annotation Manager

Boston Dynamics

Waltham, MA • On-site

$100K - $128K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 29 days ago

Job description

Boston Dynamics is a world leader in mobile robots, tackling some of the toughest robotics challenges. We combine the principles of dynamic control and balance with sophisticated mechanical designs, cutting-edge electronics, and next-generation software for high-performance robots equipped with perception, navigation, and intelligence.
The Atlas team is focused on advancing machine learning and manipulation capabilities. We are seeking a highly organized and technically-minded Annotation Manager to lead and develop a team of Annotation Leads and Annotation QA Leads, manage relationships with third-party annotation vendors and service providers, and ensure the quality, consistency, and throughput of data annotations that power Atlas's machine learning systems. This role reports to the Atlas Data Operations Associate Director.
Schedule/Working Hours:
  • Monday - Friday: 40 hours per week, regular hours (e.g., 9 AM to 6 PM, with flexibility across both first and second shift as required).

Responsibilities:
Team Leadership & Performance:
  • Directly manage a team of Annotation Leads and Annotation QA Leads, providing day-to-day direction, prioritization, coaching, and performance feedback.
  • Serve as the Atlas working team lead for third-party annotation vendors, managing task allocation, performance accountability, and dual-source relationships to optimize cost and quality.

Annotation Quality & Operations:
  • Own the end-to-end quality of annotation output by partnering with ML Leads to establish and enforce quality benchmarks, monitor throughput and error rate metrics, and serve as the primary escalation point for labeling guideline ambiguities.
  • Write SOP's and technical/process/policy documents for the annotation team to follow
  • Forecast forward-looking annotation needs across all Atlas engineering stakeholders and drive continuous improvement initiatives including tooling feedback, workflow redesign, and SOP maintenance.

Required Skills & Experience:
  • Bachelor's degree in a technical field, data science, or cognitive science preferred; proven experience managing teams in a data annotation or machine learning data pipeline environment required, with strong familiarity with annotation concepts and tooling platforms.
  • Prior experience managing third-party annotation vendors strongly preferred; strong computer proficiency with familiarity in ML data pipelines or computer vision, and exceptional organizational and communication skills required.

The base pay range for this position is between $100,000 to $128,000 annually. Base pay will depend on multiple individualized factors including, but not limited to internal equity, job related knowledge, skills and experience. This range represents a good faith estimate of compensation at the time of posting. Boston Dynamics offers a generous Benefits package including medical, dental vision, 401(k), paid time off and an annual bonus structure. Additional details regarding these benefit plans will be provided if an employee receives an offer for employment.

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