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Data Annotation Software Engineer Jobs (NOW HIRING)

NEURA Robotics

$80 - $100/hr

... software engineering * Strong programming skills in Python and C++ * Solid understanding of AI ... Experience with data annotation tools or labeling workflows * Familiarity with dataset structuring ...

Innodata Inc.

$55 - $60/hr

As the Data/Annotation Engineer, you'll be hands-on with the data itself. You'll administer the annotation toolchain, manage annotation workflows across the corpus, and produce the per-dataset ...

TEKsystems

Data Annotation Specialist

Austin, TX · Remote

$25 - $35/hr

Basic computer skills and familiarity with common software tools. Additional Skills & Qualifications Ways To Stand Out From The Crowd: Experience in data annotation or a related field. Familiarity ...

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Data Annotation Software Engineer information

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$44.5K

$129.7K

$177.5K

How much do data annotation software engineer jobs pay per year?

As of Sep 9, 2026, the average yearly pay for data annotation software engineer in the United States is $129,716.00, according to ZipRecruiter salary data. Most workers in this role earn between $114,500.00 and $137,500.00 per year, depending on experience, location, and employer.

What is a data annotation software engineer?

Data Annotation Software Engineers are professionals who design, develop, and maintain software tools and systems that enable the labeling or tagging of data for use in machine learning and artificial intelligence projects. They help automate and streamline the data annotation process, ensuring that large volumes of data—such as images, audio, or text—are accurately labeled for model training. Their work often involves developing user interfaces, integrating annotation tools with data pipelines, and optimizing workflows to improve efficiency and data quality. These engineers play a critical role in ensuring that AI models are trained on high-quality, well-labeled datasets.

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

To thrive as a Data Annotation Software Engineer, you need strong programming skills (typically in Python or Java), experience with machine learning workflows, and a degree in computer science or a related field. Familiarity with annotation tools (like Labelbox or Supervisely), data labeling platforms, and version control systems such as Git is commonly required. Attention to detail, problem-solving abilities, and effective communication are valuable soft skills in this position. These skills ensure the creation of high-quality annotated datasets, which are crucial for training accurate machine learning models.

What are the main challenges data annotation software engineers face when ensuring data quality for machine learning projects?

Data Annotation Software Engineers often encounter challenges related to maintaining high-quality, consistent labels across large and diverse datasets. Ambiguous data, evolving project requirements, and aligning annotation guidelines with real-world scenarios can make this work complex. Engineers need to develop robust tools, implement quality checks, and collaborate closely with data scientists and annotation teams to ensure that the labeled data meets project standards. Continuous feedback loops and automation can help address these issues, but attention to detail and adaptability are essential for success in this role.

What is the difference between Data Annotation Software Engineer vs Data Scientist?

AspectData Annotation Software EngineerData Scientist
Required CredentialsBachelor's in CS, Data Science, or related; experience with annotation toolsBachelor's or higher in CS, Statistics, or related; advanced analytics skills
Work EnvironmentCollaborates with ML teams, focuses on annotation tools and pipelinesAnalyzes data, builds models, interprets results
Industry UsageUsed in AI/ML development, data labeling projectsApplied in predictive modeling, data analysis, research

While both roles work within data and AI projects, Data Annotation Software Engineers primarily develop and maintain annotation tools and pipelines, focusing on data labeling processes. Data Scientists analyze and interpret data to build models. The roles often collaborate but differ in technical focus and responsibilities.

Does data annotation really pay well?

Data annotation software engineers typically earn competitive salaries that vary based on experience, location, and skill level. Entry-level roles may pay modestly, while experienced professionals with expertise in annotation tools and machine learning can command higher wages. Overall, the role offers decent pay compared to many entry-level tech positions.

What are popular job titles related to Data Annotation Software Engineer jobs?

For Data Annotation Software Engineer jobs, the most frequently searched job titles are:

Infographic showing various Data Annotation Software Engineer job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 83% Full Time, 12% Part Time, and 3% Contract. Highlights an 85% Physical, 3% Hybrid, and 12% Remote job distribution, with an average salary of $129,716 per year, or $62.4 per hour.

Data Annotation Engineer

Louisville, KY • On-site

Tanisha Systems
1 - 5K employees
Learn more about Tanisha Systemsexternal

Full-time

Posted 12 days ago

Job description

Data Annotation Engineer

Location - Louisville, Kentucky (Day 1 onsite)


Pay Rate: Market- based on experience


We are looking for an Annotation Program Lead to manage our AI Quality Annotation Program. This individual will oversee the operational execution of human review activities that establish the gold-standard datasets used to measure conversational AI performance, safety, and compliance.
The ideal candidate combines strong program management skills with experience in quality assurance, data annotation operations, and stakeholder engagement.
  • Lead the day-to-day operation of the AI annotation program.
  • Manage a team of annotators responsible for reviewing customer conversations and AI interactions.
  • Develop annotation guidelines, procedures, and quality standards.
  • Establish calibration programs and quality assurance processes to ensure consistency across reviewers.
  • Partner with Data Scientists to create and maintain gold-standard datasets.
  • Monitor annotation accuracy, throughput, and quality metrics.
  • Coordinate reporting and deliverables for Legal, Compliance, Responsible AI, and executive stakeholders.
  • Manage annotation workflows and tooling including setting up annotation jobs, running data processing scripts, and testing annotation UIs for quality before job launch.
  • Plan capacity requirements to support seasonal increases in review volume.
  • Support future expansion into multilingual evaluation programs.
  • Identify process improvements that increase efficiency and annotation quality.
  • Bachelor's degree or equivalent experience in Linguistics/Psychology.
  • 5+ years of experience in program management, operations, quality assurance, data labeling, or related fields.
  • Experience leading teams and managing operational workflows.
  • Strong organizational and stakeholder management skills.
  • Ability to analyze quality metrics and drive continuous improvement initiatives.
  • Excellent written and verbal communication skills.
  • Familiarity with Python/R, SQL and Excel. Should be familiar with running scripts on language data.
  • Strong organizational and stakeholder management skills.
  • Ability to analyze quality metrics and drive continuous improvement initiatives.
  • Excellent written and verbal communication skills.
  • Familiarity with Python/R, SQL and Excel. Should be familiar with running scripts on language data.
  • Experience supporting AI, machine learning, or data annotation programs.
  • Familiarity with Responsible AI, compliance, legal review, or governance processes.
  • Experience developing operational standards and quality frameworks.
  • Experience managing vendor or contractor resources.