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Full Time Machine Learning Data Annotation Jobs (NOW HIRING)

Encord

Human Data Operations Strategist

New York, NY · On-site

Oversee data annotation projects, translating complex AI and machine learning requirements into clear workflows and instructions for data annotation teams * Ensure the highest standards of data ...

Q ANALYSTS LLC

Data Annotation Technician

Kirkland, WA · On-site +1

... AI) and machine learning (ML). Q Analysts is headquartered in San Jose, CA with a presence ... Q Analysts is looking for Data Annotation Technicians to support Ground Truth Data Collection ...

Encord

Human Data Operations Strategist

New York, NY · On-site

Oversee data annotation projects, translating complex AI and machine learning requirements into clear workflows and instructions for data annotation teams * Ensure the highest standards of data ...

Abaka AI

Machine Learning Engineer

Mountain View, CA · On-site

... data workflows, including collection, preprocessing, annotation, versioning, and model integration. • Implement and refine training strategies for large-scale AI systems, including vision, video ...

NTENT

Machine Learning Engineer

Carlsbad, CA · On-site

Machine Learning Engineer Position: Full time Location: Carlsbad office About Us: NTENT provides a ... Coordinate data collection and annotation efforts. * Work with real-time data and content coming ...

Abaka AI

Machine Learning Engineer

Mountain View, CA · On-site

... data workflows, including collection, preprocessing, annotation, versioning, and model integration. • Implement and refine training strategies for large-scale AI systems, including vision, video ...

NTENT

Machine Learning Engineer

Carlsbad, CA · On-site

Machine Learning Engineer Position: Full time Location: Carlsbad office About Us: NTENT provides a ... Coordinate data collection and annotation efforts. * Work with real-time data and content coming ...

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All JobsMachine Learning Data Annotation JobsFull Time Machine Learning Data Annotation Jobs

Full Time Machine Learning Data Annotation information

See salary details

$37.5K

$122.7K

$196.5K

How much do full time machine learning data annotation jobs pay per year?

As of Aug 7, 2026, the average yearly pay for full time 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 are the key skills and qualifications needed to thrive as a full time machine learning data annotation specialist, and why are they important?

To thrive as a Full Time Machine Learning Data Annotation Specialist, you need strong attention to detail, basic data literacy, and familiarity with data labeling concepts, often supported by a high school diploma or equivalent. Proficiency in specialized annotation platforms, spreadsheet tools, and sometimes knowledge of Python or labeling frameworks is typically required. Reliability, patience, and effective communication are valuable soft skills for ensuring accuracy and collaborating with team members. These skills and qualities are crucial because they directly impact the quality of training data, which is essential for developing effective machine learning models.

What is a full time machine learning data annotation job?

Full time machine learning data annotation jobs involve labeling, tagging, or categorizing data such as images, text, audio, or video to help train machine learning models. Data annotators play a crucial role in ensuring that AI systems learn from high-quality, accurately labeled datasets. These positions often require attention to detail, consistency, and sometimes familiarity with the subject matter or specialized tools. Full-time roles may be remote or onsite and can span industries like autonomous vehicles, healthcare, retail, and more.

What are some common challenges faced by machine learning data annotators, and how are these typically addressed within a team?

Machine learning data annotators often encounter challenges such as maintaining consistency in labeling, handling ambiguous data, and meeting tight deadlines for large datasets. Teams usually address these by establishing clear annotation guidelines, conducting regular training sessions, and implementing quality assurance processes like peer reviews and spot checks. Collaboration with data scientists and project managers is also common, ensuring that annotators can ask questions and clarify uncertainties, leading to higher-quality labeled data and a supportive work environment.

What is the difference between Full Time Machine Learning Data Annotation vs Data Labeling Specialist?

AspectFull Time Machine Learning Data AnnotationData Labeling Specialist
CredentialsHigh school diploma or equivalent; some roles prefer technical certificationsHigh school diploma or equivalent; training often provided on the job
Work EnvironmentOffice or remote; collaborative with data science teamsRemote or office; focused on labeling tasks
Industry UsageUsed across AI/ML companies, tech firms, and startupsCommon in AI/ML, data services, and outsourcing companies
Job FocusCreating labeled datasets for machine learning modelsAnnotating data such as images, videos, or text for AI training

Full Time Machine Learning Data Annotation involves creating high-quality labeled datasets for AI models, often requiring technical understanding. Data Labeling Specialists focus on annotating data accurately, typically with less emphasis on technical skills. Both roles are essential in AI development but differ mainly in scope and technical complexity.

More about Full Time Machine Learning Data Annotation jobs
What cities are hiring for Full Time Machine Learning Data Annotation jobs? Cities with the most Full Time 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 Full Time Machine Learning Data Annotation jobs? States with the most job openings for Full Time Machine Learning Data Annotation jobs include:
What job categories do people searching Full Time Machine Learning Data Annotation jobs look for? The top searched job categories for Full Time Machine Learning Data Annotation jobs are:
Full Time Machine Learning Data Annotation jobs near you
Infographic showing various Full Time Machine Learning Data Annotation job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 83% Full Time, 12% Part Time, and 4% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $122,738 per year, or $59 per hour.

Data Technical Product Manager

Success Matcher Recruitment

San Francisco, CA • On-site

$200K - $350K/yr

Full-time

Re-posted 14 days ago

Job description

About the Opportunity

Our client, a well-funded, early-stage robotics and AI company is seeking a highly technical and execution-oriented Data Technical Product Manager (TPM) to own the end-to-end data infrastructure powering next-generation machine learning systems.


This role sits at the intersection of machine learning, data engineering, operations, and product management. You will be responsible for building and scaling the data flywheel that transforms raw data collected from deployed robotic systems into high-quality training datasets used to improve AI performance.


The ideal candidate has experience managing large-scale data operations, working closely with ML teams, and translating ambiguous technical requirements into structured execution plans.

This is a unique opportunity to join a rapidly growing robotics company building cutting-edge autonomous systems while working directly alongside world-class engineers, researchers, and founders.

What You'll Do

Own the End-to-End Data Platform

  • Drive the roadmap for the central data platform.
  • Manage the complete data lifecycle from capture through ingestion, storage, labeling, curation, and delivery to ML training pipelines.
  • Partner with engineering teams to scale infrastructure supporting large-volume datasets.


Partner with Machine Learning Teams

  • Translate ML research requirements into actionable data collection and annotation specifications.
  • Define dataset requirements and collection strategies to support model development.
  • Ensure researchers have access to reliable, high-quality training data.


Define Data Quality Standards

  • Create QA frameworks, audit processes, and validation workflows.
  • Establish standards for data quality, coverage, consistency, and labeling accuracy.
  • Identify sensor drift, data degradation, and annotation issues before they impact training outcomes.

Manage Data Annotation Operations

  • Source and manage third-party labeling vendors.
  • Define vendor performance expectations and quality metrics.
  • Conduct audits and implement continuous improvement initiatives.


Build Data Discovery & Infrastructure Capabilities

  • Partner with infrastructure and platform engineers to improve:
    • Data ingestion
    • Cataloging
    • Search
    • Versioning
    • Dataset management


Design the Data Flywheel

  • Build systems that automatically surface edge cases and production failures.
  • Create workflows that capture and route valuable operational data back into future training cycles.
  • Improve data collection efficiency and model iteration speed.


Drive Metrics & Operational Excellence

  • Define and monitor critical metrics including:
    • Throughput
    • Label quality
    • Data coverage
    • Dataset freshness
    • Drift detection
    • Operational efficiency


Required Qualifications

  • 4+ years of experience in one or more of the following:
    • Technical Product Management
    • Data Engineering
    • Large-Scale Data Operations
  • Proven experience building and managing end-to-end data pipelines.
  • Experience supporting applied machine learning or AI systems.
  • Strong understanding of data quality management and governance.
  • Ability to operate effectively in highly ambiguous, fast-moving startup environments.
  • Experience coordinating across engineering, research, operations, and external vendors.


Preferred Qualifications

  • Experience with multimodal datasets including:
    • Video
    • Sensor data
    • Point clouds
    • Telemetry
  • Robotics, autonomous systems, computer vision, or industrial AI experience.
  • Familiarity with large-scale data annotation workflows.
  • Experience designing feedback loops that improve model performance over time.