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Freelance Machine Learning Data Annotation Jobs in New York

Encord

Strategic Projects Lead

New York, NY · On-site

$150K - $300K/yr (+ commission)

The role As a Strategic Projects Lead, you'll fully own and optimize the data annotation and machine learning workflows behind Encord's largest client relationships. You'll work directly with clients ...

Encord

Strategic Projects Lead

New York, NY · On-site

$150K - $300K/yr

The role As a Strategic Projects Lead, you'll fully own and optimize the data annotation and machine learning workflows behind Encord's largest client relationships. You'll work directly with clients ...

JPMorgan Chase & Co

Software Engineer II

Jersey City, NJ · On-site

$106K - $146K/yr

... the machine learning lifecycle-spanning model operations, data development (such as processing and data annotation), and governance tooling. You will collaborate closely with engineers, system ...

Worky

Software Engineer II

Jersey City, NJ · On-site

$101K - $139K/yr

... machine learning lifecycle--spanning model operations, data development (such as processing and data annotation), and governance tooling. You will collaborate closely with engineers, system ...

JP Morgan Chase

Software Engineer II

Jersey City, NJ · On-site

$106K - $146K/yr

... the machine learning lifecycle-spanning model operations, data development (such as processing and data annotation), and governance tooling. You will collaborate closely with engineers, system ...

exacare ai

Machine Learning Engineer

New York, NY · On-site

Improve data processing , annotation workflows , and ML system efficiency * Deploy and maintain the ... Proven (3+ years) of experience in machine learning engineering, MLOps, ML infrastructure, data ...

Forhyre

Machine Learning Engineer

New York, NY · On-site

Study and transform data science prototypes * Design machine learning systems * Research and ... implement appropriate ML algorithms and tools * Develop machine learning applications according to ...

Aston Carter

Machine Learning Engineer

Manhattan, NY · On-site

$125/hr

Write, review, and optimize Python and SQL code used in data analysis and machine learning workflows. * Design realistic business and analytics use cases based on professional experience and industry ...

Aston Carter

Machine Learning Engineer

Manhattan, NY · On-site

$125/hr

Write, review, and optimize Python and SQL code used in data analysis and machine learning workflows. * Design realistic business and analytics use cases based on professional experience and industry ...

Jane Street

Machine Learning Engineer

New York, NY

Machine learning is a critical pillar of Jane Street's global business. Our ever-changing trading ... data. The interview process follows the same structure as our Software Engineering Intern ...

Jane Street

Machine Learning Engineer

New York, NY · On-site

Machine learning is a critical pillar of Jane Street's global business. Our ever-changing trading ... data. The interview process follows the same structure as our Software Engineering Intern ...

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Freelance Machine Learning Data Annotation information

What is freelance machine learning data annotation?

Freelance machine learning data annotation involves labeling or tagging data—such as images, text, audio, or video—to help train machine learning models. As a freelancer, you work independently or through platforms, completing specific annotation tasks assigned by companies or researchers. This work is essential because high-quality labeled data is required for AI systems to learn and make accurate predictions. Annotators may categorize images, transcribe speech, or highlight relevant information in documents. The flexibility of freelancing allows you to choose projects and work remotely.

What are the key skills and qualifications needed to thrive as a freelance machine learning data annotation specialist?

To thrive as a Freelance Machine Learning Data Annotation specialist, you need attention to detail, basic knowledge of data labeling concepts, and familiarity with machine learning data types. Experience with annotation tools (such as Labelbox, RectLabel, or CVAT) and understanding of data privacy protocols are commonly required. Strong communication, time management, and the ability to follow complex guidelines are essential soft skills for delivering accurate results. These skills ensure high-quality, consistent data annotation, which is critical for effective machine learning model training and performance.

What are some common challenges faced by freelance machine learning data annotators, and how can they be managed?

Freelance machine learning data annotators often encounter challenges such as maintaining data accuracy, handling repetitive tasks, and understanding complex annotation guidelines. Staying organized and regularly reviewing project instructions can help ensure consistency and quality in annotations. Additionally, communicating proactively with project managers and utilizing annotation tools efficiently can help manage workload and clarify uncertainties. Building expertise in different data types (text, image, audio) also allows annotators to diversify their projects and reduce monotony.

What is the difference between Freelance Machine Learning Data Annotation vs Data Labeler?

AspectFreelance Machine Learning Data AnnotationData Labeler
CredentialsBasic understanding of annotation tools, sometimes with specialized domain knowledgeTypically no formal credentials required
Work EnvironmentRemote, flexible, project-basedOften remote or in-house, depending on employer
Industry UsageUsed in AI/ML development for training datasetsUsed in data preparation for various industries, including AI
Search/Comparison IntentFocuses on freelance opportunities, project scope, and toolsMore general, often employed by companies for data labeling tasks

Freelance Machine Learning Data Annotation involves independently completing annotation tasks for AI models, often with specialized tools and domain knowledge. Data Labelers typically perform similar tasks but may work as employees or contractors within a company. The main difference lies in the freelance nature and project-based work of data annotation roles.

Can I work for freelance machine learning data annotation with no experience?

Freelance machine learning data annotation jobs often do not require prior experience, as many tasks involve simple labeling or categorization that can be learned quickly. Basic computer skills, attention to detail, and familiarity with annotation tools are helpful, and training is usually provided. However, building a portfolio or gaining some familiarity with data annotation platforms can improve job prospects.

What are the most commonly searched types of Machine Learning Data Annotation jobs in New York?

The most popular types of Machine Learning Data Annotation jobs in New York are:

What are popular job titles related to Freelance Machine Learning Data Annotation jobs in New York?

For Freelance Machine Learning Data Annotation jobs in New York, the most frequently searched job titles are:

What job categories do people searching Freelance Machine Learning Data Annotation jobs in New York look for?

The top searched job categories for Freelance Machine Learning Data Annotation jobs in New York are:

What cities in New York are hiring for Freelance Machine Learning Data Annotation jobs?

Cities in New York with the most Freelance Machine Learning Data Annotation job openings:

Freelance Machine Learning Data Annotation jobs near you
Infographic showing various Freelance Machine Learning Data Annotation job openings in New York as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 87% Full Time, 9% Part Time, and 2% Contract. Highlights an 84% Physical, 3% Hybrid, and 13% Remote job distribution.

Strategic Projects Lead

New York, NY • On-site

Encord
See more jobs at Encordexternal

$150K - $300K/yr (+ commission)

Full-time

Medical, Dental, Vision, PTO

Re-posted 23 days ago

Job description

About us

Encord is the universal data layer for AI that helps 300+ AI teams train and run models on the right data. Our platform indexes, curates, annotates, and evaluates data across the full AI lifecycle, from development through production.

 

Trusted by Woven by Toyota, AXA, UiPath, Zipline, and more. We're an ambitious team of 100+ working at the frontier of AI and have raised $60M in Series C funding from Wellington Management, CRV, Next47 and Y Combinator.

 

The role

As a Strategic Projects Lead, you'll fully own and optimize the data annotation and machine learning workflows behind Encord's largest client relationships. You'll work directly with clients, annotation specialists, ML engineers, Account Managers, and Forward Deployed Engineers to ensure the data powering their models is fast, accurate, and scalable.

This is a hands-on, high visibility role. You have direct influence on whether Encord's highest-value customers renew and expand.

What you'll do

  • Own data annotation projects end-to-end, translating complex AI/ML requirements into clear workflows and instructions for annotation teams

  • Design and refine annotation processes, audit results, and build feedback loops that raise data quality

  • Act as a trusted advisor to clients — designing and implementing the human-annotation workflow that gets them to production fastest

  • Partner with product and engineering to drive improvements in AI training data tools and methodology

  • Directly influence account health: your workflow design and execution are a primary driver of whether strategic accounts renew, expand, or churn

Who we're looking for

  • A sharp, execution-oriented operator with a consulting or AI-company pedigree . A structured thinker, strong PM instincts, bias for getting things done

  • Analytically rigorous and comfortable with ambiguity. You break down operational problems from first principles

  • Technically fluent: comfortable querying a database, auditing annotation outputs, or automating a workflow in Python

  • A natural translator between ML engineers and non-technical clients on multi-stakeholder projects

  • Entrepreneurial — you take ownership without waiting to be told what to do

Experience requirements

  • 3–7 years of professional experience, with a strong preference for backgrounds in top-tier strategy consulting and/or operations or data roles at leading AI or technology companies

  • Proven ownership of complex, multi-stakeholder workflows end-to-end: scoping, execution, QA, iteration

  • Experience designing/optimizing data operations with an eye for quality, consistency, and scalability, ideally human-in-the-loop or structured labeling work

  • Demonstrated ability to engage effectively with both technical stakeholders (ML engineers, data scientists) and non-technical clients, translating requirements clearly in both directions

  • Bonus: hands-on experience with computer vision, generative AI, or multimodal data workflows; prior exposure to data annotation platforms or quality management frameworks; experience coaching or managing operational teams

  • Bonus: Working proficiency in Python or SQL, with the ability to query data, automate workflows, or audit annotation outputs; broader familiarity with relational databases or data annotation tooling equally valued

Why Encord

  • Competitive salary, commission, and meaningful equity in a high-growth start-up

  • Clear, accelerated growth opportunities as the company scales rapidly

  • Strong in-person culture: 4 days/week

  • Flexible PTO to fully recharge

  • Annual learning & development budget

  • Comprehensive health, dental, and vision coverage

  • Frequent travel opportunities across the U.S., London, and Europe

  • Bi-annual company offsites, twice-weekly team lunches, and monthly socials

Compensation Range: $150K - $300K