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Machine Learning Data Annotation Jobs (NOW HIRING)

Apple

Machine Learning Data Engineer

Cupertino, CA

$181K - $318K/yr

Description We are seeking a highly experienced and strategic Machine Learning Data Engineer to drive our machine learning data with a strong focus on quality. In this role, you will transform ...

Trimble

Machine Learning Data Scientist

Lake Oswego, OR

$122K - $165K/yr

Essential Skills & Experience * 5+ years of expertise in data science or engineering, specifically building and deploying predictive machine learning models. * Proficiency in Python and SQL for data ...

Trimble

Machine Learning Data Scientist

Westminster, CO

$122K - $165K/yr

Essential Skills & Experience * 5+ years of expertise in data science or engineering, specifically building and deploying predictive machine learning models. * Proficiency in Python and SQL for data ...

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Data Annotator (Autonomous Vehicle / AI)

San Francisco, CA · On-site

$32 - $39/hr

... to power machine learning models. This position plays a critical role in ensuring the safety ... The ideal candidate is detail-oriented, technically proficient in 2D and 3D annotation, and ...

Mercor

Data Scientist | Upto $100/hr

New York, NY · Remote

$100/hr

... experience in Machine Learning , Data Science , Software Engineering , Computer Science ... Prior experience with data annotation, labeling, evaluation, or human feedback collection.

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All JobsMachine Learning Data Annotation Jobs

Machine Learning Data Annotation information

What is machine learning data annotation?

Machine learning data annotation is the process of labeling or tagging data—such as images, text, audio, or video—so that it can be used to train machine learning models. Annotators add relevant information to raw data, helping algorithms learn to recognize patterns and make predictions. This process is essential for supervised learning, as models require accurately labeled datasets to achieve high performance. Data annotation can be done manually or with the help of specialized tools, and is a critical step in developing reliable AI systems.

What are the key skills and qualifications needed to thrive as a Machine Learning Data Annotation Specialist, and why are they important?

To thrive as a Machine Learning Data Annotation Specialist, you need strong attention to detail, familiarity with data labeling processes, and a basic understanding of machine learning concepts, often supported by a relevant degree or specialized training. Experience with annotation platforms such as Labelbox, Supervisely, or CVAT, and knowledge of data management systems are commonly required. Diligence, consistency, and effective communication are essential soft skills for ensuring high-quality annotated datasets and collaborating with machine learning teams. These skills are crucial for producing accurate training data, which directly impacts the performance and reliability of AI models.

What is the difference between Machine Learning Data Annotation vs Data Labeler?

AspectMachine Learning Data AnnotationData Labeler
CredentialsBasic computer skills, attention to detailBasic computer skills, attention to detail
Work EnvironmentRemote or on-site, tech companies, AI projectsRemote or on-site, data processing centers, AI companies
Industry UsageAI, machine learning, data scienceData management, AI, machine learning
Job FocusCreating labeled datasets for training AI modelsLabeling data to assist AI training

Machine Learning Data Annotation involves creating detailed labels and annotations for datasets used to train AI models, often requiring understanding of specific data types. Data Labelers focus on applying labels to data, typically with less emphasis on complex annotations. Both roles are essential in AI development, but data annotation often involves more specialized tasks and tools.

What are some common challenges faced in a Machine Learning Data Annotation role, and how can they be addressed?

One common challenge in a Machine Learning Data Annotation role is maintaining high consistency and accuracy, especially when dealing with large volumes of complex data. Ambiguities in labeling guidelines or unclear data points can also make the work more difficult. To address these issues, annotators often participate in regular training sessions, utilize detailed instruction manuals, and collaborate closely with quality assurance teams. Open communication with project managers and peers is also essential to clarify uncertainties and ensure alignment with project standards.
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Machine Learning Data Annotation jobs near you
Infographic showing various Machine Learning Data Annotation job openings in the United States as of June 2026, with employment types broken down into 100% Full Time. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.
Machine Learning / Data Scientist

Machine Learning / Data Scientist

Parsons Corporation

Washington, DC • On-site

$88K - $154K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 21 days ago

Parsons rating

7.9

Company rating: 7.9 out of 10

Based on 44 frontline employees who took The Breakroom Quiz

158th of 356 rated engineering

Job description

In a world of possibilities, pursue one with endless opportunities. Imagine Next!
At Parsons, you can imagine a career where you thrive, work with exceptional people, and be yourself. Guided by our leadership vision of valuing people, embracing agility, and fostering growth, we cultivate an innovative culture that empowers you to achieve your full potential. Unleash your talent and redefine what's possible.
Job Description:
We have a career opportunity for a Machine Learning / Data Scientist to develop advanced analytical models and experiments that enhance decision-making, improve forecasting, and uncover insights across mission and support activities. This role would be based in Washington, DC. Would be required to be on-site
This role will support the enablement of machine learning capabilities within our analytics environment, working closely with data analysts, engineers, and program stakeholders.
The Machine Learning / Data Scientist is a hands-on practitioner with strong capabilities in model development, data preparation, and analytical storytelling. This role requires the ability to frame complex problems, select appropriate modeling approaches, implement and validate models, and communicate results in accessible terms to non-technical audiences.
Key Responsibilities:
  • Design and implement machine learning and advanced analytics solutions that address operational, programmatic, or strategic questions.
  • Collaborate with stakeholders to define analytical problems, identify relevant data, and translate business needs into modeling requirements.
  • Prepare and engineer features from multiple data sources, ensuring data quality and suitability for modeling.
  • Develop, train, and validate models (e.g., classification, regression, clustering, forecasting) using appropriate techniques and tools.
  • Evaluate model performance, perform error analysis, and refine approaches to improve accuracy, robustness, and interpretability.
  • Integrate model outputs into dashboards, applications, or automated workflows, in coordination with analytics and development teams.
  • Document modeling approaches, assumptions, and results, and communicate findings through clear narratives and visualizations.
  • Support experimentation and pilot projects that explore new analytical techniques and tools.
  • Contribute to the development of standards and practices for responsible and sustainable use of advanced analytics.

Typical Assignments:
  • Building predictive or prescriptive models that support prioritization of projects, resource allocation, or risk assessment.
  • Conducting exploratory data analysis to identify patterns, anomalies, and opportunities for improved performance.
  • Developing prototypes of ML-enabled features that can be integrated into existing dashboards or applications.
  • Supporting performance management by providing advanced analyses of trends and drivers underlying key metrics.
  • Collaborating with data management staff to ensure datasets are suitable for modeling and reproducible analysis.
  • Preparing technical and non-technical presentations that summarize modeling methods, findings, and implications.

Education and Experience:
  • Bachelor's degree in Data Science, Statistics, Computer Science, Mathematics, or a closely related field; a master's degree is preferred but not required, or equivalent work experience.
  • 5+ years of experience in data science, machine learning, or advanced analytics roles.
  • Demonstrated experience developing, validating, and deploying machine learning models using tools such as Python, R, or equivalent.
  • Strong background in statistics, model evaluation, and experimental design.
  • Experience with data preparation, feature engineering, and working with complex, multi-source datasets.
  • Familiarity with integrating model outputs into BI tools or applications (e.g., via APIs, embedded analytics) is preferred.
  • Experience working within mission-oriented or public sector environments (e.g., DHS, DoD) is a plus.

Security Clearance Requirement:
None
This position is part of our Critical Infrastructure team.
For more than 80 years, our experts have designed and delivered the critical infrastructure that connects and protects communities around the world. We work in collaborative teams, both within the company and with our partners and customers, to plan, design, build, and modernize infrastructure. We take special pride in projects and solutions that improve communities as well as people's quality of life by promoting economic growth, enhancing mobility, and increasing sustainability and resiliency. Powered by our people, we provide the imagination necessary to support our customers' visions-and to help them see what's next!
Salary Range: $88,400.00 - $154,700.00
We value our employees and want our employees to take care of their overall wellbeing, which is why we offer best-in-class benefits such as medical, dental, vision, paid time off, Employee Stock Ownership Plan (ESOP), 401(k), life insurance, flexible work schedules, and holidays to fit your busy lifestyle!
Parsons is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, veteran status or any other protected status.
We truly invest and care about our employee's wellbeing and provide endless growth opportunities as the sky is the limit, so aim for the stars! Imagine next and join the Parsons quest-APPLY TODAY!
Parsons is aware of fraudulent recruitment practices. To learn more about recruitment fraud and how to report it, please refer to https://www.parsons.com/fraudulent-recruitment/.

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