ZipRecruiter

Log In

1

Data Annotation Project Manager Jobs in New York

Mecka AI

Human Data Architect, Quality

New York, NY · On-site

$130K - $160K/yr

This is a standards-and-methodology architecture role, not a QA-management role . You set the ... Who You Are Required Background * 5+ years working at the intersection of ML and data - annotation ...

Zealogics.com

Data Center Project Manager

Somerset, NJ · On-site

$127K/yr

... managing MEP or mission-critical construction projects within data centers or a similar environment required. • Experience with financial firms is preferred as it relates to the critical nature of ...

Turing

Strategic Project Lead

New York, NY · On-site

Design and manage data pipelines from customer specification to final delivery, with full ... Own quality control across the annotation lifecycle: set the bar, measure against it, and close the ...

Mercor

Legal Professional - Fully Remote

New York, NY · Remote

$55 - $135/hr

Prior experience in legal data annotation or AI/ML projects . * Familiarity with CourtListener . Application Process (Takes 20-30 mins to complete) * Upload resume * AI interview based on your resume

Mercor

Senior Attorney | Upto $135/hr

New York, NY · Remote

$135/hr

Prior experience in legal data annotation or AI/ML projects . * Familiarity with CourtListener . Application Process (Takes 20-30 mins to complete) * Upload resume * AI interview based on your resume

Mercor

Legal Expert - Court Opinion Analyst

New York, NY · On-site +1

$55 - $135/hr

Prior experience in legal data annotation or AI/ML projects . * Familiarity with CourtListener . Application Process (Takes 20-30 mins to complete) * Upload resume * AI interview based on your resume

Mercor

Attorney

New York, NY · On-site +1

$55 - $135/hr

Prior experience in legal data annotation or AI/ML projects . * Familiarity with CourtListener . Application Process (Takes 20-30 mins to complete) * Upload resume * AI interview based on your resume

next page

Showing results 1-20

All JobsData Annotation Project Manager JobsData Annotation Project Manager Jobs in New York

Data Annotation Project Manager information

What is the average salary for a data annotation project manager?

The average salary for a data annotation project manager typically ranges from $70,000 to $110,000 annually, depending on experience, location, and company size. In regions with a high cost of living, such as California, salaries tend to be higher to compensate for living expenses.

Does data annotation really pay you?

Data annotation project managers oversee labeling tasks and typically earn a salary or hourly wage, depending on the employer and project scope. Compensation varies based on experience, location, and the complexity of the annotation work, but it is generally a paid role with standard employment benefits. Freelance or contract annotators may be paid per task or project.

What are the key skills and qualifications needed to thrive as a Data Annotation Project Manager, and why are they important?

To thrive as a Data Annotation Project Manager, you need strong project management skills, a solid understanding of data annotation processes, and experience with quality assurance, often supported by a degree in a relevant field. Familiarity with annotation tools (like Labelbox or Supervisely), workflow management platforms, and sometimes agile or PMP certification is highly beneficial. Exceptional communication, attention to detail, and leadership abilities help you effectively coordinate teams and ensure project deliverables meet quality standards. These skills are essential for managing complex annotation projects efficiently, maintaining data integrity, and supporting successful machine learning outcomes.

Is data annotation real or fake?

Data annotation is a real and essential process in machine learning and AI development, involving labeling data such as images, text, or audio to train algorithms. Data annotation project managers oversee this work, ensuring accuracy and quality using tools like labeling platforms. The process is legitimate and widely used in industry for creating reliable datasets.

What is the highest salary of data annotation?

The highest salaries for data annotation project managers can reach up to $80,000 to $100,000 annually, depending on experience, location, and the complexity of projects managed. Senior roles with extensive oversight or specialized skills in tools like labeling platforms may earn higher compensation. Salary ranges vary widely based on industry and company size.

What are some common challenges faced by Data Annotation Project Managers, and how can they be managed effectively?

One of the primary challenges Data Annotation Project Managers face is ensuring high-quality, consistent labeling across large and sometimes distributed annotation teams. Managing tight deadlines while maintaining annotation accuracy requires effective training, clear guidelines, and regular quality checks. Additionally, balancing communication between data scientists, clients, and annotators is crucial to align expectations and resolve ambiguities quickly. Successful managers often implement robust feedback loops, leverage annotation tools with built-in quality control features, and foster an open environment for continuous improvement.

What is the difference between Data Annotation Project Manager vs Data Labeling Specialist?

AspectData Annotation Project ManagerData Labeling Specialist
CredentialsTypically requires project management experience, certifications in data management or related fieldsOften requires basic technical skills, familiarity with labeling tools, sometimes certifications in data annotation
Work EnvironmentOversees teams, manages projects, coordinates workflows in office or remote settingsPerforms labeling tasks, often in a remote or on-site environment, focused on data tagging
Employer & Industry UsageUsed by tech companies, AI firms, and data service providers for managing annotation projectsEmployed within similar industries, focusing on executing labeling tasks under supervision

The main difference is that the Data Annotation Project Manager oversees and coordinates annotation projects, ensuring quality and deadlines, while the Data Labeling Specialist focuses on executing the labeling tasks themselves. Both roles are essential in the data annotation process but differ in responsibilities and scope.

What is a Data Annotation Project Manager?

A Data Annotation Project Manager is responsible for overseeing projects that involve labeling and categorizing data, such as images, text, or audio, to train machine learning models. They coordinate teams of annotators, manage project timelines, and ensure the quality and accuracy of the annotated data. This role often acts as a bridge between data scientists, clients, and annotation teams, ensuring project requirements are met efficiently and effectively.
What are popular job titles related to Data Annotation Project Manager jobs in New York? For Data Annotation Project Manager jobs in New York, the most frequently searched job titles are:
What job categories do people searching Data Annotation Project Manager jobs in New York look for? The top searched job categories for Data Annotation Project Manager jobs in New York are:
What cities in New York are hiring for Data Annotation Project Manager jobs? Cities in New York with the most Data Annotation Project Manager job openings:
Data Annotation Project Manager jobs near you

Human Data Architect, Quality

Mecka AI

New York, NY • On-site

$130K - $160K/yr

Full-time

Posted 10 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