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Data Annotation For Ai Jobs in Union, NJ (NOW HIRING)

Lockedinai

AI Analysis Specialist

Manhattan, NY · On-site

$100 - $125/hr

Develop data annotation guidelines, quality scoring rubrics, and validation processes to ensure ... for model evaluation * Collaborate with data engineering to build and maintain automated data ...

micro1 AI

AI Trainer - Microbiology Expert

Newark, NJ · Remote

$70 - $90/hr

Document experimental findings and processes with a focus on clarity for AI training data ... Experience with AI, machine learning, or annotation projects related to biology or microbiology.

micro1 AI

AI Trainer - Microbiology Expert

New York, NY · Remote

$70 - $90/hr

Document experimental findings and processes with a focus on clarity for AI training data ... Experience with AI, machine learning, or annotation projects related to biology or microbiology.

micro1 AI

AI Trainer - Microbiology Expert

Paterson, NJ · Remote

$70 - $90/hr

Document experimental findings and processes with a focus on clarity for AI training data ... Experience with AI, machine learning, or annotation projects related to biology or microbiology.

Aston Carter

Paralegal / AI Trainer

Manhattan, NY · On-site

$125/hr

Comfort working with AI tools, AI training workflows, data annotation, and large language models ... Individual compensation offered for this position within this range will depend on many factors ...

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Data Annotation For Ai information

What is data annotation for AI?

Data annotation for AI is the process of labeling or tagging data—such as text, images, audio, or video—to make it understandable for machine learning models. Annotators add relevant information to raw data, helping AI systems learn to recognize patterns and make accurate predictions. This step is crucial for training, validating, and testing AI algorithms, especially in tasks like computer vision and natural language processing. High-quality data annotation directly impacts the effectiveness and reliability of AI applications.

What are some common challenges faced by data annotators working on AI projects, and how can they be addressed?

Data annotators for AI often encounter challenges such as maintaining consistency across large datasets, understanding ambiguous labeling instructions, and managing repetitive tasks. To address these issues, it's important to actively seek clarification on guidelines, participate in team discussions to align on labeling standards, and use annotation tools that flag inconsistencies. Regular feedback sessions with project leads also help improve accuracy and efficiency, fostering a collaborative and supportive work environment.

What are the key skills and qualifications needed to thrive as a data annotation specialist for AI, and why are they important?

To thrive as a Data Annotation Specialist for AI, you need a keen eye for detail, a solid understanding of data labeling concepts, and often a background in the relevant domain (such as language, images, or audio). Proficiency with annotation platforms, data management systems, and basic familiarity with tools like Excel or Python can be highly valuable. Strong communication, consistency, and time management skills help ensure accuracy and meet project deadlines. These abilities are crucial because high-quality, well-annotated data is foundational for training reliable and effective AI models.

What is the difference between Data Annotation For Ai vs Data Labeler?

AspectData Annotation For AiData 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 companies
Industry UsageArtificial Intelligence, Machine LearningData management, content moderation
Job FocusPreparing data for AI algorithms through annotationLabeling data for various purposes, including AI

Data Annotation For Ai involves preparing datasets specifically for training AI models, focusing on detailed annotations. Data Labeler is a broader role that includes labeling data for multiple purposes, including AI but also other data management tasks. While both roles require similar skills, Data Annotation For Ai is more specialized towards AI development projects.

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Data Annotation For Ai jobs near you

Team Leader - Code Generation, Data AI

Bloomberg LP

New York, NY • On-site

$125K - $150K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 19 days ago

Key responsibilities

  • Provide technical and operational leadership across client-facing code generation and agent workstreams, aligning leads on priorities, technical direction, quality standards, and delivery expectations.

  • Establish shared technical and evaluation practices for natural language to code, structured generation, and agent workflows, focusing on correctness, reliability, and client experience.

  • Define and improve evaluation methodologies for generated code and structured outputs, including correctness, semantic fidelity, execution quality, robustness, and failure analysis.

Bloomberg rating

9.4

Company rating: 9.4 out of 10

Based on 11 frontline employees who took The Breakroom Quiz

11th of 247 rated software companies

Job description

Team Leader - Code Generation, Data AI
Location
New York
Business Area
Data
Ref #
10053334
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 improve our systems, products and processes - all while providing customer support to our clients.
Our Team:
The Bloomberg Data AI group brings modern AI technologies into Bloomberg's Data organization while contributing deep financial domain expertise to the development of AI-powered products. We partner closely with partners 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 the impact of the Data organization by delivering intelligent data solutions and domain-informed systems that improve the capabilities and competitiveness of Bloomberg's offerings.
What's the Role?
As Bloomberg's AI-powered code generation products continue to grow in scale and importance, we are seeking a Team Leader to provide technical and operational leadership across workstreams focused on improving the quality, reliability, and effectiveness of client-facing AI experiences. This is a hands-on leadership role focused on connecting work across a highly autonomous team, establishing shared technical and quality standards, and helping workstreams move from experimentation to scalable production.
You will act as a force multiplier for the team by providing technical direction, resolving ambiguity, identifying dependencies, and enabling workstream leads to execute independently while remaining aligned to a common strategy. A key part of the role will be advancing technical practices across the team, strengthening how we evaluate correctness and failure modes, and expanding the use of automation and tooling to improve client outcomes. Working closely with Product, Engineering, Data partners, and the CTO's Office, you will help translate client needs, financial domain expertise, and model behavior into measurable improvements in Bloomberg's code generation capabilities.
We'll Trust You To:
  • Provide technical and operational leadership across client-facing code generation and agent workstreams, aligning leads on priorities, technical direction, quality standards, and delivery expectations.
  • Establish shared technical and evaluation practices for natural language to code, structured generation, and agent workflows, with a focus on correctness, reliability, and client experience.
  • Partner with Engineering, Product, AI teams, and domain experts to translate client and product needs into scalable generation, evaluation, annotation, and improvement strategies.
  • Define and improve evaluation methodologies for generated code and structured outputs, including correctness, semantic fidelity, execution quality, robustness, and failure analysis.
  • Drive improvements in client-facing AI outcomes by scaling the tooling, automation, data pipelines, annotation workflows, and evaluation frameworks used to identify and address model and system failures.
  • Serve as a technical escalation point and force multiplier by resolving ambiguity, identifying cross-workstream dependencies, advancing technical practices, and contributing hands-on to high-priority problems.

You'll Need to Have:
  • Significant experience in applied AI, machine learning, code generation, evaluation, data, software engineering, or a closely related technical field.
  • Experience providing technical leadership across multiple projects or workstreams, including influencing peers and leading through expertise rather than formal authority.
  • Strong technical fluency and the ability to engage credibly with Engineering and AI partners on prompts, schemas, APIs, data structures, evaluation design, and system behavior.
  • Strong understanding of generative AI workflows and how evaluation data, annotation, grounding, structured representations, and quality measurement can be used to improve model and product performance.
  • Experience evaluating generated code or structured outputs using metrics, error analysis, execution results, and other signals of correctness, reliability, and production readiness.
  • Strong analytical, communication, and coordination skills, with the ability to turn ambiguous client or system problems into structured, measurable improvement strategies across technical, product, and domain partners.

We'd Love to See:
  • Experience building or evaluating natural language to code, query generation, agentic systems, or other client-facing generative AI applications.
  • Hands-on experience with Python, SQL, APIs, structured data, schemas, data pipelines, or similar technical tooling.
  • Familiarity with BQL or other domain-specific query languages.
  • Experience designing evaluation frameworks, automated evaluation systems, benchmarking approaches, error taxonomies, or annotation strategies for generative AI.
  • Familiarity with execution-based evaluation, root-cause analysis, issue discovery, production monitoring, or closed-loop AI quality improvement workflows.
  • Experience in financial services or another complex, domain-rich environment, with a track record of mentoring technical contributors and raising technical capabilities across a team.

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.
Discover what makes Bloomberg unique - watch our podcast series for an inside look at our culture, values, and the people behind our success.

What Bloomberg employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

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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

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