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Data Annotation Engineer Jobs in New York (NOW HIRING)

Mercor

Network Engineer - Data Annotation

New York, NY · Remote

$50 - $70/hr

Network Engineer - Data for Autonomous Systems annotation Type: Contract Compensation: $50-$70/hour Location: Remote Commitment: 30-40 hours/week Role Responsibilities * Review real-world data from ...

Cohere

Data Annotation Specialist, Safety

New York, NY · On-site

We're training and deploying frontier models for developers and enterprises who are building AI ... As a Data Annotation Specialist on safety task, you will: * Evaluate and improve model safety:

New

Infotree Global Solutions

Data Engineer-AI/ML

Manhattan, NY · On-site

$126.20K - $151.60K/yr

Machine Translation Data Engineer Onsite 3 days per week in any of these locations: Seattle, NYC ... Experience at developing linguistic annotation projects (e.g. Machine Translation, ASR ...

Alignerr

Data Platform Engineer (Python)

Manhattan, NY · Remote

Data Platform Engineer (Python) About the Role What if your Python expertise could directly shape ... Develop full-stack tooling and backend services for data annotation, validation, and quality ...

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Data Annotation Engineer information

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$56.3K

$161.3K

$215.5K

How much do data annotation engineer jobs pay per year?

As of May 28, 2026, the average yearly pay for data annotation engineer in New York is $161,327.00, according to ZipRecruiter salary data. Most workers in this role earn between $91,900.00 and $214,400.00 per year, depending on experience, location, and employer.

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 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.

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.
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Data Annotation Engineer jobs near you
Infographic showing various Data Annotation Engineer job openings in New York as of May 2026, with employment types broken down into 5% As Needed, 67% Full Time, 23% Part Time, and 5% Contract. Highlights an 49% Physical, and 51% Remote job distribution, with an average salary of $161,327 per year, or $77.6 per hour.

Backend Developer - Data Annotation Systems

Alignerr

Manhattan, NY • Remote

Other

This job post has expired today. Applications are no longer accepted.

Job description

Backend Developer - Data Annotation Systems (AI Training)
About the Role
What if your Python expertise could directly shape the infrastructure behind the most advanced AI systems in the world? We're looking for a Senior Python Full-Stack Engineer to design and build the data pipelines, annotation tooling, and evaluation systems that leading AI labs depend on to train and improve next-generation models.
This is a fully remote, flexible contract role for an experienced engineer who thrives on high-impact systems work and wants to be close to the frontier of AI development.
  • Organization
    : Alignerr
  • Type
    : Hourly Contract
  • Location
    : Remote
  • Commitment
    : 20-40 hours/week
What You'll Do
  • Design, build, and optimize high-performance Python systems that power AI data pipelines and model evaluation workflows
  • Develop full-stack backend services and tooling for large-scale data annotation, validation, and quality control
  • Build and maintain asynchronous task queues to handle complex, long-running background jobs at scale
  • Optimize database queries for high-read/write workloads and serve data via real-time protocols such as WebSockets
  • Improve reliability, performance, and safety across existing Python codebases
  • Collaborate closely with data, research, and engineering teams to support model training and evaluation workflows
  • Identify bottlenecks and edge cases in system and data behavior, then implement scalable, production-ready fixes
  • Participate in synchronous design reviews to iterate on architecture and implementation decisions
Who You Are
  • Native or fluent English speaker with clear written and verbal communication skills
  • Full-stack developer with a strong systems programming background and 3-5+ years of professional Python experience
  • Proven experience building and shipping production-grade Python applications
  • Experienced with asynchronous task queues and background job processing
  • Skilled at optimizing database performance for demanding, high-throughput applications
  • Comfortable working with real-time data protocols (e.g., WebSockets)
  • Self-directed and reliable - able to commit 20-40 hours per week and deliver consistently without hand-holding
Nice to Have
  • Prior experience with data annotation, data quality pipelines, or model evaluation infrastructure
  • Familiarity with AI/ML workflows, model training, or benchmarking systems
  • Experience with distributed systems, developer tooling, or data engineering
Why Join Us
  • Work directly with leading AI labs on production systems that matter
  • Fully remote and flexible - structure your work around your schedule
  • Freelance autonomy with the substance of high-impact engineering work
  • Get hands-on exposure to the cutting edge of AI infrastructure and research workflows
  • Potential for ongoing work and contract extension as projects scale

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