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Ai Data Annotation Jobs in Illinois (NOW HIRING)

YO AI Labs

English Language Expert - Remote

Chicago, IL · Remote

$30 - $50/hr

Previous experience in AI data training, annotation, editorial QA, localization QA, or professional copyediting . * Experience evaluating AI-generated content or training data. * Strong hands-on ...

YO AI Labs

English Language Expert - Remote

Chicago, IL · Remote

$30 - $50/hr

Previous experience in AI data training, annotation, editorial QA, localization QA, or professional copyediting . * Experience evaluating AI-generated content or training data. * Strong hands-on ...

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Ai Data Annotation information

What is an AI data annotation?

An AI Data Annotation job involves labeling or tagging data, such as text, images, audio, or video, to train machine learning models. Annotators ensure that data is accurately categorized so AI systems can learn to recognize patterns and make predictions. This work is crucial for improving AI applications like self-driving cars, chatbots, and image recognition software. It often requires attention to detail and familiarity with specific annotation tools.

What does an AI data annotation do?

As an AI Data Annotator, your typical day involves labeling and tagging data such as images, audio, or text according to specific project guidelines, often using specialized annotation software. You may work independently or as part of a remote or on-site team, collaborating with data scientists and quality assurance specialists to ensure consistency and accuracy. Regular feedback sessions and quality checks are common to maintain high annotation standards. The role can be repetitive, but attention to detail and clear communication with team members help create datasets that are crucial for training effective AI systems.

What are the key skills and qualifications needed to thrive in AI data annotation?

To excel in AI Data Annotation, you need strong attention to detail, data accuracy, and a basic understanding of data labeling concepts, typically supported by a high school diploma or equivalent. Familiarity with annotation tools such as Labelbox, Supervisely, or similar platforms is often required, and some employers may value basic programming or machine learning course certifications. Excellent communication, the ability to follow detailed guidelines, and time management are valuable soft skills in this role. These skills ensure the production of high-quality annotated datasets, which are critical for training reliable AI and machine learning models.

What are the most commonly searched types of Ai Data Annotation jobs in Illinois?

The most popular types of Ai Data Annotation jobs in Illinois are:

What are popular job titles related to Ai Data Annotation jobs in Illinois?

For Ai Data Annotation jobs in Illinois, the most frequently searched job titles are:

What job categories do people searching Ai Data Annotation jobs in Illinois look for?

The top searched job categories for Ai Data Annotation jobs in Illinois are:

Ai Data Annotation jobs near you
Infographic showing various Ai Data Annotation job openings in Illinois as of August 2026, with employment types broken down into 58% Full Time, 31% Part Time, and 11% Contract. Highlights an 54% In-person, and 46% Remote job distribution.

Machine Learning Engineer, Frontier AI Evaluation (Contract)

Cobalt

Mundelein, IL • On-site

Other

Posted 6 days ago

Key responsibilities

  • Produce written reasoning traces on real ML engineering tasks, diagnosing failures and explaining fixes.

  • Author ML engineering problems and task environments with automated success checks.

  • Evaluate model-generated ML code and configurations, ranking solutions and identifying points of failure.

Job description

About the role:

Cobalt is seeking machine learning engineers to produce the expert reasoning, task environments, and evaluation data used to train and assess frontier AI models on real ML engineering work.

This opportunity is suited to practitioners rather than only researchers: ML engineers, applied scientists, MLOps and platform engineers, and data engineers who have trained, deployed, and maintained models in production. A PhD is welcome but not required, and hands-on delivery experience counts for more here than publication record.

You do not need prior experience in data annotation or AI research. What matters is that you can diagnose why a pipeline or a training run is failing, decide what the right fix is, and explain both clearly enough for another engineer to follow.


What you'll do:

Depending on the project, you may:

  • Produce written reasoning traces on real ML engineering tasks, capturing how you diagnose a failing training run, a data pipeline defect, or a serving regression, including what you rule out and why
  • Author non-trivial ML engineering problems and task environments with checks that verify success automatically, including multi-file and multi-step tasks
  • Evaluate model-generated ML code and configurations, ranking solutions, explaining what makes the stronger one stronger, and identifying the point at which the approach goes wrong
  • Assess whether a proposed solution actually addresses the failure, and identify fixes that pass the immediate check but mask the underlying problem, degrade performance, or would not survive review
  • Design rubrics and partial-credit criteria for scoring multistep engineering tasks, and classify observed failures into a consistent taxonomy

Projects follow their own guidelines, formatting conventions, and quality standards, and you will work with feedback from reviewers and lab research teams.


Required qualifcations:

  • Several years of hands-on experience building, training, and deploying machine learning systems in production, with a track record you can point to
  • Strong coding ability in Python, plus working command of at least one deep learning framework such as PyTorch or JAX, and comfort reading unfamiliar codebases
  • Depth in at least one area, for example large-scale training and distributed compute, data pipelines and feature infrastructure, model serving and inference optimization, evaluation and monitoring, or fine-tuning and post-training workflows
  • Solid debugging discipline, including the ability to isolate a failure across data, model, and infrastructure rather than guessing at it
  • Ability to explain each step of your reasoning clearly in writing, and to produce work another engineer could reproduce and review


Why join Cobalt AI:

  • Advance frontier AI where it counts. Apply your expertise to data that frontier labs cannot obtain any other way, where your reasoning directly shapes how the next generation of models works through technical problems.
  • Grow professionally. Expand your influence through evaluation projects, advisory roles, and research collaborations, while developing a working understanding of how frontier models are trained and assessed.
  • Work with a top-tier network. Collaborate with researchers and engineers from leading institutions and labs on high-impact, flexible work.
  • Set your own schedule. Flexible 10 to 40 hour weeks that fit around your existing work and your life.
  • Competitive pay. Rates vary by project and are determined by a number of factors, including scope, skillset, and experience.

Apply