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Data Annotation Engineer Jobs in New Jersey (NOW HIRING)

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Data Annotation Engineer information

See New Jersey salary details

$52.3K

$149.7K

$200K

How much do data annotation engineer jobs pay per year?

As of Jul 20, 2026, the average yearly pay for data annotation engineer in New Jersey is $149,708.00, according to ZipRecruiter salary data. Most workers in this role earn between $85,300.00 and $199,000.00 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 in the Data Annotation Engineer position, and why are they important?

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.

Does data annotation really pay?

Data annotation engineers can earn competitive wages, often paid hourly or per task, with pay rates varying based on experience, complexity of annotations, and the platform or employer. Entry-level roles may start at minimum wage, while experienced annotators or those with specialized skills can earn higher salaries or freelance rates. Overall, data annotation can provide a reliable income, especially for remote or flexible work arrangements.

What is the highest salary for data annotator?

The highest salary for a data annotation engineer can reach up to $80,000 to $100,000 annually, depending on experience, location, and the complexity of annotation tasks. Senior roles or those with specialized skills in tools like Labelbox or CVAT may earn higher compensation. Salaries vary widely across companies and regions but generally reflect the technical skills required for high-quality data labeling.

What is a data annotation engineer?

A data annotation engineer is a professional responsible for labeling and annotating data, such as images, text, or videos, to prepare it for machine learning models. They often use specialized tools and follow guidelines to ensure data quality, supporting the development of AI systems.

How hard is it to get hired by data annotation?

Getting hired as a data annotation engineer typically requires basic computer skills, attention to detail, and familiarity with annotation tools. Many positions are entry-level and may not require advanced degrees, but strong accuracy and consistency are important for success in the role.

What is a Data Annotation Engineer job?

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.

What are popular job titles related to Data Annotation Engineer jobs in New Jersey? For Data Annotation Engineer jobs in New Jersey, the most frequently searched job titles are:
What job categories do people searching Data Annotation Engineer jobs in New Jersey look for? The top searched job categories for Data Annotation Engineer jobs in New Jersey are:
What cities in New Jersey are hiring for Data Annotation Engineer jobs? Cities in New Jersey with the most Data Annotation Engineer job openings:
Infographic showing various Data Annotation Engineer job openings in New Jersey as of July 2026, with employment types broken down into 83% Full Time, and 17% Contract. Highlights an 90% In-person, and 10% Remote job distribution, with an average salary of $149,708 per year, or $72 per hour.
Team Leader - Data Engineering (Shared Infrastructure)

Team Leader - Data Engineering (Shared Infrastructure)

Bloomberg LP

Princeton, NJ • On-site

$120K - $144K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 16 days ago


Bloomberg rating

9.4

Company rating: 9.4 out of 10

Based on 11 frontline employees who took The Breakroom Quiz

11th of 209 rated software companies


Job description

Team Leader - Data Engineering (Shared Infrastructure)
Location
Princeton
Business Area
Data
Ref #
10051710
Description & Requirements
Bloomberg runs on data. Our products are fueled by powerful information. We combine data and context to paint the whole picture for our clients, around the clock - from around the world. In Data, we are responsible for delivering this data, news and analytics through innovative technology - quickly and accurately. We apply problem-solving skills to identify innovative workflow efficiencies, and we implement technology solutions to enhance our systems, products and processes - all while providing customer support to our clients.
Our Team:
The Data AI group brings innovative AI technologies into the Data organization while contributing deep financial domain expertise to the development of AI-powered products. We partner closely with stakeholders to align AI innovation with Bloomberg's strategic objectives, focusing on optimizing data workflows and elevating the quality, intelligence, and usability of the data that drives our products. Our work amplifies impact by delivering intelligent data solutions and domain-informed systems that enhance the capabilities and competitiveness of Bloomberg's offerings.
What's the Role?
We are seeking a Team Leader to drive our Shared Infrastructure programs within Data. This role is central to enabling scalable and responsible data workflows by leading a team passionate about reusable infrastructure, integration patterns, and operational standards across the organization. You will partner closely with Engineering, Product, and Data teams to identify high-impact opportunities, define and implement reusable solutions, and enable consistent, efficient workflows across a distributed set of teams. This includes crafting how capabilities such as automated evaluation and LLM-enabled annotation are adopted and integrated into production workflows, in close collaboration with partner teams who own underlying platforms.
The ideal candidate is a thoughtful and pragmatic leader who combines deep technical fluency with a systems-oriented approach and an interest in emerging data and LLM-based workflows. You are able to translate sophisticated, evolving needs into clear, reusable approaches and scalable patterns, and you are comfortable operating in environments where ownership is distributed across teams. You bring experience contributing to and scaling shared data systems or frameworks, with exposure to evaluation workflows and LLM-enabled pipelines, and a clear perspective on how these capabilities should be integrated into production environments. You excel at identifying patterns across disparate team needs and translating them into well-defined requirements, reusable solutions, and adoption strategies. You are effective at driving alignment and securing partner consensus, particularly in ambiguous environments where success depends on influence rather than direct ownership. You have a track record of building communities of practice and guiding teams toward consistent, scalable approaches without relying on formal authority.
We'll Trust You To:
  • Lead and develop a central team responsible for defining and delivering shared data infrastructure and reusable workflow patterns that improve consistency and efficiency across teams
  • Provide technical and strategic leadership, translating diverse team needs into clear requirements, scalable solutions, and well-defined approaches to data workflows, automated evaluation, and efficient annotation
  • Partner closely with Engineering, Product, and Data leaders to identify high-impact opportunities for shared capabilities, align on priorities, and ensure solutions can be optimally put into production within technical, compliance, and cost constraints
  • Act as a central point of coordination for cross-team needs, identifying common gaps, reducing duplication, and enabling consistent approaches without becoming a bottleneck or enforcement layer
  • Ensure shared components, frameworks, and patterns are well-designed, well-documented, and broadly adopted, with a focus on usability, scalability, and real-world impact
  • Mentor and develop data engineers, encouraging a culture of pragmatism, strong technical judgment, and a focus on building solutions that are both scalable and widely usable

You'll Need to Have:
  • Prior people leadership experience, ideally guiding teams working on technical, data, or infrastructure-related problems in multi-functional environments.
  • Strong technical judgment in data engineering and shared systems design, with the ability to engage credibly with engineering partners on architecture, trade-offs, and scalable solutions.
  • Experience designing and scaling shared frameworks, systems, or platform-like capabilities across multiple teams
  • Experience with LLM-enabled workflows or annotation pipelines.
  • Proven ability to operate in ambiguous, high-judgment environments, translating diverse needs into clear requirements and practical, scalable solutions
  • Proven track record of driving alignment and influencing partners across engineering, product, and data teams without direct authority
  • Strong analytical and decision-making skills, with a track record of delivering clear, well-reasoned, and impactful solutions

We'd Love to See:
  • Familiarity with evaluation or data quality frameworks
  • Exposure to regulated or cost-constrained environments
  • Experience partnering with engineering to scale prototypes into production
  • Background in platform, infrastructure, or centralized enablement teams
  • Experience contributing to communities of practice or developer enablement efforts

Salary Range = 135,000 - 230,000 USD Annual + Benefits + Bonus
The referenced salary range is based on the Company's good faith belief at the time of posting. Actual compensation may vary based on factors such as geographic location, work experience, market conditions, education/training and skill level.
We offer one of the most comprehensive and generous benefits plans available and offer a range of total rewards that may include merit increases, incentive compensation (exempt roles only), paid holidays, paid time off, medical, dental, vision, short and long term disability benefits, 401(k) +match, life insurance, and various wellness programs, among others. The Company does not provide benefits directly to contingent workers/contractors and interns.
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About Bloomberg

Sourced by ZipRecruiter

Bloomberg runs on data. As the Data Management & Analytics team within Engineering, we support our organization's needs around managing data efficiently. The vision of the team is to build solutions that drive data quality, data dictionary, data stewardship, data lineage, reference, and master data management across various data domains (prospect, customer, vendor, material etc.). We partner with business teams across the organization in addressing their data needs and ultimately helping run business operations efficiently and make improved decisions.

Industry

Finance and insurance

Company size

10,000+ Employees

Headquarters location

New York, NY, US

Year founded

1981