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Data Annotation Jobs in Edison, NJ (NOW HIRING)

Mecka AI

Human Data Architect, Quality

New York, NY · On-site

$130K - $160K/yr

Who You Are Required Background * 5+ years working at the intersection of ML and data - annotation methodology, dataset curation, data-centric ML, ground truth design, or labeling-specifications work ...

Welo Data

Data Labeling Associate

New York, NY

$34/hr

WHAT YOU'LL DO • Execute Data labelling and annotation tasks across speech and voice datasets. • Work with audio and language data, including transcription, categorization, and tagging. YOU ARE A ...

Smarsh

Lead Data Scientist

New York, NY · On-site +1

Data annotation and quality review * Exploratory data analysis and model fail state analysis * Contribute to model governance, documentation, and explainability frameworks aligned with internal and ...

Smarsh

Lead Data Scientist

New York, NY · On-site

Data annotation and quality review * Exploratory data analysis and model fail state analysis * Contribute to model governance, documentation, and explainability frameworks aligned with internal and ...

Mercor

Bilingual Evaluator - LLM Specialist

New York, NY · Remote

$15 - $20/hr

Prior experience with RLHF, model evaluation, or data annotation work * Experience writing or editing high-quality written content * Experience comparing multiple outputs and making fine-grained ...

Distyl AI

Research Engineers, Data

New York, NY · On-site

$150K - $250K/yr

Develop synthetic data, annotation, and feedback-loop strategies to improve system performance in areas where real-world data is sparse or noisy * Analyze customer workflows and datasets to determine ...

Mercor

Bilingual Evaluator - Odia Specialist

New York, NY · Remote

$15 - $20/hr

Prior experience with RLHF, model evaluation, or data annotation work . * Experience writing or editing high-quality written content . * Experience comparing multiple outputs and making fine-grained ...

Mercor

Bilingual Analyst - Punjabi Expert

New York, NY · Remote

$15 - $20/hr

Experience with RLHF, model evaluation, or data annotation work * Experience writing or editing high-quality written content * Experience comparing multiple outputs and making fine-grained ...

Mercor

Bilingual Evaluator - Telugu Expert

New York, NY · Remote

$15 - $20/hr

Prior experience with RLHF, model evaluation, or data annotation work . * Experience writing or editing high-quality written content . * Experience comparing multiple outputs and making fine-grained ...

Mercor

Bilingual Evaluator - Marathi Expert

New York, NY · Remote

$15 - $20/hr

Prior experience with RLHF, model evaluation, or data annotation work . * Experience writing or editing high-quality written content . * Experience comparing multiple outputs and making fine-grained ...

Mercor

Bilingual Analyst - Bengali Expert

New York, NY · Remote

$15 - $20/hr

Prior experience with RLHF, model evaluation, or data annotation work * Experience writing or editing high-quality written content * Experience comparing multiple outputs and making fine-grained ...

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All JobsData Annotation JobsData Annotation Jobs in Edison, NJ

Data Annotation information

What does a typical workday look like for someone in a Data Annotation role?

A typical workday as a Data Annotator involves reviewing datasets—such as images, audio, text, or video—and accurately labeling or categorizing information according to specific project guidelines. Most Data Annotators work independently, but they often collaborate with project managers or data scientists to clarify requirements and resolve ambiguities. Tasks may be repetitive, but adhering to precise standards is vital for maintaining data quality. Work environments can range from technology companies to remote or freelance settings, and advancement opportunities exist as team leads or quality assurance specialists for those who excel in consistency and reliability.

Is data annotation a genuine job?

Data annotation is a legitimate job that involves labeling data such as images, text, or audio to help train machine learning models. It often requires attention to detail and familiarity with annotation tools, and can be found in various industries like technology and healthcare.

Does data annotation pay well?

Data annotation jobs typically offer entry-level pay that varies depending on the employer, location, and complexity of the tasks. While some positions pay hourly wages comparable to other administrative or clerical roles, experienced annotators working on specialized projects or with advanced tools can earn higher rates. Overall, data annotation is often considered an entry-level position with moderate pay potential.

What is a Data Annotation job?

A Data Annotation job involves labeling and categorizing data, such as text, images, audio, or video, to help train machine learning models. Annotators apply tags, bounding boxes, or classifications to data based on specific guidelines. This process improves the accuracy of AI systems in recognizing patterns and making predictions. Many data annotation jobs require attention to detail and familiarity with specific domains. It is commonly used in applications like autonomous driving, natural language processing, and computer vision.

How hard is it to get hired by data annotation?

Getting hired for a data annotation role generally requires basic computer skills, attention to detail, and sometimes familiarity with specific tools or platforms. Many positions are entry-level and do not require advanced education, making the hiring process relatively accessible, though competition can vary based on the employer and location.

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

To thrive in Data Annotation, you need strong attention to detail, accuracy, and basic data handling skills, often supported by a high school diploma or equivalent. Familiarity with annotation platforms, data labeling software, or content management systems is frequently required, though specific certifications are rare. Excellent communication, time management, and the ability to focus on repetitive tasks distinguish top performers in this role. These skills are crucial because accurate and consistent data annotation directly impacts the quality of machine learning models and AI applications.

What does a data annotator do?

A data annotator labels and tags data such as images, text, or videos to help machine learning models understand and learn from the data. They use tools and follow guidelines to ensure accuracy and consistency, often working with large datasets in a structured environment. Attention to detail and knowledge of annotation tools are important for this role.
What are popular job titles related to Data Annotation jobs in Edison, NJ? For Data Annotation jobs in Edison, NJ, the most frequently searched job titles are:
What job categories do people searching Data Annotation jobs in Edison, NJ look for? The top searched job categories for Data Annotation jobs in Edison, NJ are:
What cities near Edison, NJ are hiring for Data Annotation jobs? Cities near Edison, NJ with the most Data Annotation job openings:
Data Annotation jobs near you
Infographic showing various Data Annotation job openings in Edison, NJ as of July 2026, with employment types broken down into 60% Full Time, 30% Part Time, and 10% Contract. Highlights an 70% In-person, and 30% Remote job distribution.

Human Data Architect, Quality

Mecka AI

New York, NY • On-site

$130K - $160K/yr

Full-time

Posted 21 days ago

Job description

About Mecka AI
Mecka AI is building the data and deployment infrastructure for embodied intelligence. We collect, curate, and license the world's most useful robotics training data to leading AI labs, and we deploy real robotic systems with enterprise customers across hospitality, retail, QSR, pharmacy, logistics, and healthcare. We work with the foundation model teams shaping the next decade of robotics, and with the operators running real businesses today. Quality, trust, and execution are core to our partnerships.
The Role
We're hiring a Human Data Architect, Quality to be the person with taste for what robotics training data should look like at Mecka. You will define what good data is - the labeling rubrics, ontologies, schemas, sampling philosophy, and acceptance criteria that every dataset we ship is measured against. You decide what goes in or out of a dataset and why.
This is a standards-and-methodology architecture role, not a QA-management role. You set the quality bar; data operations and QA teams enforce it. Your output is the spec the entire data org and our customers run on.
You will work shoulder-to-shoulder with foundation-model researchers at our customers to translate model behavior into data structure - what to label, how to label it, how to organize it, how to compose a training set, what the edge cases are, and what makes a dataset trainable versus merely large.
What You'll Own
Labeling Rubrics & Quality Criteria (per customer)
  • Define the labeling rubrics, severity levels, rejection taxonomies, and acceptance criteria for each customer program across video, sensor streams, trajectories, action labels, task outcomes, language grounding, and metadata.
  • Translate ambiguous customer requirements ("we want a model that can do X") into precise, measurable, executable data specifications.
  • Maintain customer-specific quality criteria and the canonical data dictionary every program references.
  • Build golden datasets, reference examples, and calibration tasks that define "correct" by demonstration, not just description.
Ontology & Data Organization
  • Own the taxonomy, schema, and class hierarchies for robotics datasets - how attributes are structured, how temporal segmentation works, how event boundaries are defined, how ambiguity is handled, how edge cases are categorized.
  • Decide how data is organized end-to-end so it is trainable, queryable, and composable across customers and modalities.
  • Set dataset versioning conventions, schema evolution rules, and the data-organization philosophy the org runs on.
Dataset Composition - What's In, What's Out
  • Own the philosophy for what goes into a dataset and what gets cut: distribution, diversity, edge-case representation, redundancy, license/provenance constraints.
  • Decide sampling strategies, balancing rules, and curation principles for each program.
  • Make taste-driven calls on what data is worth collecting at all - and push back when collection plans won't produce trainable data.
  • Define the acceptance bar that says "this dataset is ready to ship" - and hold it under deadline pressure.
Methodology Iteration from Model Signal
  • Iterate rubrics and ontology based on model-failure signal from customers - your standards evolve with what models actually struggle to learn.
  • Run cross-customer reviews of recurring quality misses and translate them into standards improvements.
  • Partner with engineering on automated validation (schema completeness, duplicates, time sync, metadata coverage, model-assisted review) so the standard is enforceable at scale.
Who You Are
Required Background
  • 5+ years working at the intersection of ML and data - annotation methodology, dataset curation, data-centric ML, ground truth design, or labeling-specifications work for autonomy, vision, or multimodal teams.
  • Hands-on experience designing taxonomies, ontologies, or labeling schemas that fed production model training (not just internal analytics).
  • Strong data instincts: you can open a dataset in SQL, a notebook, or Python and tell us what's wrong with it within an hour.
  • Comfortable reading ML papers and translating model-architecture needs into data-structure choices.
Strong Signals
  • Built a labeling rubric, ontology, or ground-truth spec that a large annotation org executed against in production.
  • Worked directly with research scientists at frontier AI labs or autonomy companies on what training data should contain.
  • Background in computer vision, robotics, cognitive science, linguistics, or a related field where taxonomy design is craft.
  • Have strong opinions about data quality you can defend with concrete examples.
You Are
  • A taste-maker. You believe data quality is a design problem, not a process problem.
  • Precise about definitions and obsessive about edge cases.
  • Confident saying "this dataset isn't useful and here's why" - to customers, to leadership, to research teams.
  • Energized by deciding the standard, not by managing the team that enforces it.
Why This Role
  • Define the data standards the foundation-model teams shaping the next decade of robotics will train on.
  • Be the person with the pen on what good robotics data looks like - across video, sensors, trajectories, and language.
  • Work directly with researchers at frontier AI labs, not through a sales or PM layer.
  • Build the methodology backbone of a data company at the moment the field is still deciding what "good" means.
What Success Looks Like
  • Every major customer program has a clear, documented quality standard, ontology, and acceptance criteria authored by you.
  • The data organization runs against a canonical schema and rubric set - not ad-hoc per-project decisions.
  • Customer rejection rates fall and dataset usefulness rises because the right data is being collected and labeled the right way the first time.
  • Researchers at customer labs treat you as the technical counterpart they want to talk to about what they're actually buying.
  • Standards evolve continuously from model-failure signal, not in annual rewrites.

Apply