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Data Annotation Software Engineer Jobs (NOW HIRING)

FieldAI

Senior Data Operations Engineer

Boston, MA · On-site

$110K - $140K/yr

Build and maintain annotation software and other internal data tooling. Data Collection ... It's built to grow: the Data Operations Engineering track is expected to scale from one person ...

FieldAI

Senior Data Operations Engineer

Boston, MA

$110K - $140K/yr

Build and maintain annotation software and other internal data tooling. Data Collection ... It's built to grow: the Data Operations Engineering track is expected to scale from one person ...

FieldAI

Senior Data Operations Engineer

Boston, MA · On-site

$110K - $140K/yr

Build and maintain annotation software and other internal data tooling. Data Collection ... It's built to grow: the Data Operations Engineering track is expected to scale from one person ...

JPMorgan Chase & Co.

Software Engineer II

Wilmington, DE · On-site

$94K - $129K/yr

As a Software Engineer II at JPMorgan Chase within the Corporate Artificial Intelligence and ... Design and deliver services for data preparation, annotation workflows, lineage, and auditability

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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.

AI Software Engineer Expert - Remote

Philadelphia, PA • Remote

YO AI Labs
See more jobs at YO AI Labsexternal

$100 - $200/hr

Part-time

Posted 29 days ago

Job description

Job Title: AI Software Engineering Domain Remote

Job Type: Contractor (Part-Time)
Location: Remote

Job Overview

We are seeking experienced AI Software Engineering Domain Experts to contribute their technical expertise to an innovative project focused on improving next-generation AI systems. In this role, you will evaluate, review, and refine AI-generated software engineering content to enhance the quality, accuracy, and reasoning of AI models. No prior AI experience is required—your software engineering expertise is what matters most.

Key Responsibilities
  • Analyze, review, and improve AI-generated software engineering content for technical accuracy and clarity.
  • Create, refine, and evaluate prompts to improve AI-generated technical outputs.
  • Conduct rubric-based evaluations of AI model responses, providing detailed quality feedback.
  • Draft and edit technical documentation, architecture documents, RFCs, design specifications, and engineering proposals.
  • Perform independent research and fact-checking to validate technical information.
  • Interpret and annotate technical data to support AI model training and evaluation.
  • Collaborate remotely with cross-functional teams to deliver high-quality project outcomes.
Required Skills
  • Critical Thinking
  • Analytical Reasoning
  • Quality Assurance
  • Prompt Engineering
  • AI Output Evaluation
  • Technical Documentation
  • Technical & Professional Writing
  • Content Review & Editing
  • Data Annotation
  • Fact Checking
  • Independent Research
  • Business Communication
  • Problem-Solving
  • Attention to Detail
Preferred Qualifications
  • 3+ years of professional experience as a Software Engineer, Senior Software Engineer, Staff Engineer, Technical Lead, Engineering Manager, Solutions Architect, or similar role.
  • Experience authoring or reviewing technical documentation, architecture documents, RFCs, design specifications, engineering proposals, postmortems, technical blogs, or code reviews.
  • Strong critical thinking, analytical reasoning, and structured problem-solving skills.
  • Excellent written communication and technical editing abilities.
  • Experience with AI coding tools or automated documentation tools is a plus but not required.
  • Advanced degree (Master's, MBA, JD, or PhD) or equivalent professional experience is preferred.