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Data Annotation Project Manager Jobs in New York

Mercor

Image QA Reviewer - AI Lab

New York, NY · Remote

$50 - $60/hr

Collaborate with Researchers and Ops teams to scope data projects, define quality criteria, and ... or annotation work (any domain). * Basic ability to work independently and manage one's time.

blueStone

Big Data - Project Manager

Stamford, CT

$55.25 - $74.75/hr

Providing project management support to IT management for data foundation work stream * Managing and guiding a team to carry out detail project management activities effectively on a day to day ...

MDMS Recruiting

Project Manager, Market Data

Manhattan, NY · Hybrid

$57 - $77.25/hr

Project Manager , Market Data Location: New York, NY (Hybrid), Local Candidates only Duration: 6 Months + Description: We are seeking an experienced Project Manager to anchor and drive the delivery ...

JS Consulting

Data Center Project Manager

Somerset, NJ · On-site

$127K/yr

Job Title- Data Center Project Manager Project Location - Hybrid in Somerset, New Jersey Duration- 12+ months contract Visa- USC/ GC Interview- F2F Need local with Banking exp Position Description ...

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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.
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Data Annotation Project Manager jobs near you

Rust Software Engineer - Distributed Systems

Alignerr

Manhattan, NY • Remote

Other

Posted 7 days ago

Job description

Senior Rust Software Engineer - Distributed Systems (AI Infrastructure)
About the Role
What if your deep knowledge of Rust and distributed systems could directly shape the infrastructure powering the world's most advanced AI models? We're looking for Senior Rust Engineers to build and optimize the high-performance data pipelines, annotation tooling, and evaluation systems that leading AI labs depend on every day.
This is a fully remote, flexible contract role for engineers who thrive on hard problems - concurrency, distributed state, systems performance, and scale. If you've spent years writing production Rust and want your work to matter, this is where it counts.
  • Organization
    : Alignerr
  • Type
    : Hourly Contract
  • Location
    : Remote
  • Commitment
    : 20-40 hours/week
What You'll Do
  • Design, build, and optimize high-performance Rust systems supporting large-scale AI data pipelines and evaluation workflows
  • Develop full-stack backend services and tooling for data annotation, validation, and quality control
  • Improve reliability, safety, and performance across existing production Rust codebases
  • Collaborate with data, research, and engineering teams to support model training and evaluation infrastructure
  • Identify bottlenecks, edge cases, and failure modes - then implement scalable, robust fixes
  • Participate in synchronous design reviews and iterate rapidly on architecture and implementation decisions
Who You Are
  • 3-5+ years of professional experience writing production-grade Rust
  • Strong background building distributed services using RPC frameworks and managing distributed state or consensus
  • Experienced debugging complex concurrency issues - deadlocks, race conditions - using async instrumentation and tracing tools
  • A clear communicator, both in writing and in technical discussions
  • Able to commit 20-40 hours per week consistently
  • Native or fluent English speaker
Nice to Have
  • Prior experience with data annotation, data quality pipelines, or evaluation systems
  • Familiarity with AI/ML workflows, model training, or benchmarking infrastructure
  • Background in distributed systems design or developer tooling
  • Experience contributing to or reviewing production systems at scale
Why Join Us
  • Work on real, high-stakes infrastructure used by leading AI research labs
  • Fully remote and flexible - work from anywhere, on a schedule that suits you
  • Freelance autonomy paired with the structure of meaningful, technically challenging work
  • Collaborate with top engineers and researchers working at the frontier of AI development
  • Potential for ongoing engagement and expanded scope as new projects launch

Apply