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Data Annotation Engineer Jobs (NOW HIRING)

You will work closely with cross-functional teams, including clients, annotation specialists, and machine learning engineers, to ensure high-quality data is available for AI models. What you'll do

You will work closely with cross-functional teams, including clients, annotation specialists, and machine learning engineers, to ensure high-quality data is available for AI models. What you'll do

Data Operations Engineer

Mountain View, CA · On-site

$136K - $163K/yr

They are seeking a Data Operations Engineer to own and operate the internal dataset library ... with data annotation, labeling workflows, or dataset preparation for machine learning. • ...

Data Operations Engineer

Mountain View, CA · On-site

$136K - $163K/yr

The Data Operations Engineer will manage the internal dataset library and collaborate with various ... with data annotation, labeling workflows, or dataset preparation for machine learning. • ...

Data Operations Engineer

Mountain View, CA · On-site

$136K - $163K/yr

The Data Operations Engineer will own and operate the internal dataset library, ensuring fast and ... with data annotation, labeling workflows, or dataset preparation for machine learning. • ...

Partner with data science, engineering, and product teams to align annotation efforts with model development needs * Translate clinical workflows into structured annotation schemas * Participate in ...

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Data Annotation Engineer information

See salary details

$51.5K

$147.5K

$197K

How much do data annotation engineer jobs pay per year?

As of Aug 6, 2026, the average yearly pay for data annotation engineer in the United States is $147,461.00, according to ZipRecruiter salary data. Most workers in this role earn between $84,000.00 and $196,000.00 per year, depending on experience, location, and employer.

What are the main challenges faced by data annotation engineers in their daily work?

One of the main challenges Data Annotation Engineers face is ensuring consistent accuracy and quality in labeling large and often complex datasets. Attention to detail is critical, as even small errors can significantly affect machine learning model performance. Additionally, engineers must frequently adapt to evolving annotation guidelines and emerging data types, which requires ongoing learning and flexibility. Collaboration with data scientists and project managers is common to clarify requirements and resolve ambiguities, making strong communication skills essential for success.

What are the key skills and qualifications needed to thrive as a data annotation engineer?

To thrive as a Data Annotation Engineer, you need a strong background in data analysis, attention to detail, and familiarity with annotation processes, often supported by a degree in computer science or a related field. Proficiency with annotation tools like Labelbox, CVAT, or VIA, and understanding of data formats used in machine learning, is commonly required. Excellent communication, collaboration, and organizational skills help you effectively manage projects and cooperate with cross-functional teams. These abilities are crucial for delivering high-quality labeled data, which directly impacts the performance of AI and machine learning models.

What is a data annotation engineer?

A Data Annotation Engineer is responsible for labeling and annotating data—such as text, images, audio, or video—to train machine learning models. They ensure that data is accurately categorized and structured to improve model performance. This role often involves using specialized annotation tools, following detailed guidelines, and working closely with data scientists and AI teams. Data Annotation Engineers play a crucial role in the development of AI applications by providing high-quality labeled datasets for supervised learning.

More about Data Annotation Engineer jobs
What cities are hiring for Data Annotation Engineer jobs? Cities with the most Data Annotation Engineer job openings:
What states have the most Data Annotation Engineer jobs? States with the most job openings for Data Annotation Engineer jobs include:
Infographic showing various Data Annotation Engineer job openings in the United States as of July 2026, with employment types broken down into 67% Full Time, 11% Part Time, and 22% Contract. Highlights an 89% In-person, and 11% Remote job distribution, with an average salary of $147,461 per year, or $70.9 per hour.

Technical Product Manager - Data Annotation & Labelling

Skild AI

San Mateo, CA

$190K - $219K/yr

Other

Re-posted 18 days ago


Job description

Position Overview

We are looking for a Technical Product Manager - Data Annotation & Labelling with 5+ years of experience to lead and scale the full operations lifecycle for robotics data collection. This individual will manage a cross-functional team, build scalable systems, and make a significant impact in a rapidly evolving space. This role is crucial for driving execution and continuously improving workflows and systems to support rapid growth. This is a high visibility role that will have enormous impact on the company's trajectory. 

Responsibilities
  • Own and scale the full lifecycle for products pertaining to robotics data collection, labelling and annotation from physical setups to contractor management and annotation pipelines.
  • Drive data operations programs collaborating with operations managers, technicians and engineering
  • Build 0-1 solutions for large scale data pipelines
  • Work with executive leadership to develop data operations strategy and align these to overall corporate goal
Preferred Qualifications
  • 5+ years of experience in a fast-paced, startup-like environment
  • 2+ years in a technical role (e.g., engineer, program manager, product manager) at a technology company
  • Strong technical problem-solving skills, with the ability to quickly learn complex systems
  • Proven track record of supporting cross-functional stakeholders across customers, product, and engineering
  • Proven ability to communicate effectively with senior management
  • Ability to define and drive technology strategy
  • Previous entrepreneurial experience
  • Experience building products or initiatives from 0 to 1
  • BS/MS in Technical discipline