1

Data Labelling Jobs in Boston, MA (NOW HIRING)

next page

Showing results 1-20

Data Labelling information

See Boston, MA salary details

$50K

$179.3K

$264.5K

How much do data labelling jobs pay per year?

As of Jul 5, 2026, the average yearly pay for data labelling in Boston, MA is $179,276.00, according to ZipRecruiter salary data. Most workers in this role earn between $145,000.00 and $184,700.00 per year, depending on experience, location, and employer.

What does a data labeler do?

A data labeler is responsible for annotating and categorizing data such as images, videos, or text to help train machine learning models. They use tools and guidelines to ensure accurate labeling, which is essential for developing reliable AI systems. Attention to detail and understanding of the data are important for this role.

Is data labelling a good career?

Data labelling is a common entry-level role in data annotation and machine learning workflows, often requiring attention to detail and familiarity with labeling tools. It can offer flexible schedules and opportunities to develop skills in AI and data management, but typically involves repetitive tasks and lower pay compared to more advanced tech roles.

What is a Data Labelling job?

A Data Labelling job involves annotating data, such as text, images, audio, or video, to help train machine learning models. Labelers categorize or tag data by following specific guidelines to ensure accuracy and consistency. This process is essential for improving AI applications, including image recognition, natural language processing, and autonomous systems. Attention to detail and adherence to instructions are key skills required for this role.

What is the job description of data labeling?

Data labeling involves annotating or tagging data such as images, text, or videos to help machine learning models understand and learn from the data. The role requires attention to detail, familiarity with labeling tools, and adherence to guidelines to ensure high-quality annotations for AI training. It is often performed remotely and may involve repetitive tasks with a focus on accuracy.

What are the typical daily responsibilities of a Data Labelling professional?

Data Labelling professionals are generally responsible for reviewing and accurately annotating large volumes of data—such as images, audio, video, or text—to support machine learning and AI projects. This often involves using specialized labeling platforms and following detailed guidelines provided by data scientists or project managers. You may also participate in regular team meetings to discuss quality standards or address ambiguities in data, and your work is typically reviewed for accuracy before being integrated into training datasets. Collaborating with other data annotators, engineers, and analysts is a common part of the process to ensure consistency and high-quality results.

What are the key skills and qualifications needed to thrive in the Data Labelling position, and why are they important?

To thrive as a Data Labelling professional, you need strong attention to detail, proficiency with data annotation processes, and a basic understanding of machine learning concepts. Familiarity with annotation tools like Labelbox, Supervisely, or Amazon SageMaker Ground Truth is often required, and some roles may value certifications in data processing or AI fundamentals. Reliability, patience, and the ability to follow precise instructions are important soft skills for success in this position. These skills ensure accurate and consistent data labeling, which is critical for developing effective AI models and maintaining data integrity.

How can I get started in data labeling?

To start in data labeling, gain familiarity with annotation tools like Labelbox or CVAT and understand data privacy requirements. Basic skills in image, text, or audio annotation are helpful, and some roles may require attention to detail and the ability to follow guidelines. Entry-level positions often provide training, making it accessible for beginners.
What are the most commonly searched types of Data Labelling jobs in Boston, MA? The most popular types of Data Labelling jobs in Boston, MA are:
What are popular job titles related to Data Labelling jobs in Boston, MA? For Data Labelling jobs in Boston, MA, the most frequently searched job titles are:
What job categories do people searching Data Labelling jobs in Boston, MA look for? The top searched job categories for Data Labelling jobs in Boston, MA are:
Infographic showing various Data Labelling job openings in Boston, MA as of June 2026, with employment types broken down into 3% As Needed, 30% Full Time, 46% Part Time, and 21% Contract. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution, with an average salary of $179,276 per year, or $86.2 per hour.
Project Perseus \u007C Data Labeling Associate - Italian Speakers (Human-in-the-Loop AI)

Project Perseus \u007C Data Labeling Associate - Italian Speakers (Human-in-the-Loop AI)

Welo Data

Boston, MA • On-site

$34/hr

Full-time

Posted 9 days ago

Be an early applicant


Job description

Overview

Welo Data is looking for sharp, curious, and detail-oriented individuals to join our team as Data Labeling Associate.

This is not a traditional annotation role.

You’ll be working directly with cutting-edge AI systems — evaluating outputs, identifying gaps, and helping improve how these systems behave in real-world scenarios. The work sits at the intersection of data quality, model evaluation, and human judgment, where your ability to think critically matters just as much as following guidelines.

We’re looking for people who are naturally curious about AI, comfortable forming opinions, and confident in contributing to conversations with teammates, leads, and stakeholders.

Project Details

  • Job Title: Data Labeling Associate
  • Hiring in: NYC, Seattle, Bellevue, Redmond, San Francisco, Sunnyvale, Burlingame, Austin, Los Angeles, Washington DC, Chicago, Boston
  • Hours: Full-time, 40 hours per week
  • Employment Type: W2 Full-Time Employee
  • Work Authorization: Must be authorized to work in the U.S. (no visa sponsorship)
  • Pay Rate: $34/hour
  • Contract Duration: 1-year contract with possibility of extension
Important: This is a 100% onsite position — remote work is not available for this role. To be considered, candidates must be located in or able to commute to one of the following cities: New York City, Seattle, Bellevue, Redmond, San Francisco, Sunnyvale, Burlingame, Austin, Los Angeles, Washington DC, Chicago, Boston. Please only apply if you meet this location requirement.
What You’ll Do
  • Evaluate AI model outputs and provide structured, high-quality feedback
  • Perform audit-based reviews of data and model behavior — identifying patterns, edge cases, and failure modes
  • Apply guidelines thoughtfully — and flag when they don’t reflect real-world scenarios
  • Contribute to improving evaluation frameworks, not just executing them
  • Identify trends in model performance and communicate insights clearly
  • Participate in team discussions, calibrations, and stakeholder syncs
  • Partner with leads and cross-functional teams to refine quality standards
  • Document findings in a clear, concise, and actionable way
What We’re Looking For
  • Native-level language proficiency and a university degree (Bachelor’s or higher).
  • B2 or superior level of English.  
  • 1–2 years of professional writing experience with strong, structured writing skills
  • Ability to apply complex writing rules and guidelines consistently
  • Strong understanding of safety considerations in GenAI data delivery, with 2+ years of relevant experience
  • Strong critical thinking and attention to detail
  • Ability to make sound judgment calls in ambiguous situations
  • Naturally curious about AI, technology, and how systems behave
  • Comfortable speaking up, asking questions, and contributing ideas
  • Strong written and verbal communication skills
  • Ability to stay consistent while working with evolving guidelines
  • Experience in data quality, QA, annotation, or analysis is helpful — but not required
Benefits
  • Paid Vacation: 6 days
  • Paid Company Holidays: 2 days (Memorial Day and Labor Day)
  • Paid Sick Leave: accrued per applicable state law and company policy
  • Medical, Dental, and Vision Insurance (eligibility applies)
  • Health Savings Account (HSA)
  • 401(k) Retirement Plan
  • Employee Assistance Program
  • Additional voluntary benefits (life, accident, critical illness, etc.)
  • Free Gourmet Food: Free breakfast, lunch, and dinner are provided, featuring a wide variety of cuisines in multiple cafes.
  • Micro-kitchens & Snacks: Offices are stocked with free snacks and beverages, including premium coffee and La Croix.
  • Unique Campus Features: Some locations include roof-top nature parks
  • Commuter Benefits: Free transport, shuttles, and sometimes bike-to-work perks.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.


Working at Welo Data

What to expect from working at Welo Data

From Welo Data

About Welo Data, in their own words

From Welo Data

Welo Data is a global AI data services company powering the next generation of AI. We build, annotate, and validate the training datasets that make AI models accurate, safe, and ready for the real world — across languages, cultures, and domains.

Our team of experts spans the globe, combining deep technical knowledge with a human-centered approach. If you want your work to shape how AI understands the world, you'll find your place here.

Diversity and inclusion statement

From Welo Data

Our Strength is derived from Winning Together. Welo Data is unequivocally committed to developing and fostering a workplace and organizational culture that values the diversity of thought and perspective delivered by a diverse global workforce operating within an inclusive organization.