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Data Annotation Project Manager Jobs in McHenry, IL

S and J Plumbing

Project Manager

Arlington Heights, IL · On-site

$60 - $80/hr

Competencies for Success * 3+ years of experience in a project management role, preferably in a ... Exceptional attention to detail and ability to make data-driven decisions. * Ability to multitask ...

S and J Plumbing

Project Manager

Arlington Heights, IL · On-site

Competencies for Success * 3+ years of experience in a project management role, preferably in a ... Exceptional attention to detail and ability to make data-driven decisions. * Ability to multitask ...

S and J Plumbing

Project Manager

Arlington Heights, IL · On-site

$60K - $80K/yr

Competencies for Success * 3+ years of experience in a project management role, preferably in a ... Exceptional attention to detail and ability to make data-driven decisions. * Ability to multitask ...

Actalent

Marketing Project Manager

Mettawa, IL · On-site

$50 - $55/hr

Work with agencies and internal teams to ensure accurate and timely data loading into marketing ... Strong project management skills. * Salesforce Marketing Cloud program management experience.

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All JobsData Annotation Project JobsData Annotation Project Manager JobsData Annotation Project Manager Jobs in McHenry, IL

Data Annotation Project Manager information

See McHenry, IL salary details

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How much do data annotation project manager jobs pay per hour?

As of Sep 5, 2026, the average hourly pay for data annotation project manager in McHenry, IL is $56.61, according to ZipRecruiter salary data. Most workers in this role earn between $48.99 and $66.25 per hour, depending on experience, location, and employer.

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 the key skills and qualifications needed to thrive as a data annotation project manager?

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.

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 are popular job titles related to Data Annotation Project Manager jobs in McHenry, IL?

For Data Annotation Project Manager jobs in McHenry, IL, the most frequently searched job titles are:

What job categories do people searching Data Annotation Project Manager jobs in McHenry, IL look for?

The top searched job categories for Data Annotation Project Manager jobs in McHenry, IL are:

What cities near McHenry, IL are hiring for Data Annotation Project Manager jobs?

Cities near McHenry, IL with the most Data Annotation Project Manager job openings:

Data Annotation Project Manager jobs near you
Infographic showing various Data Annotation Project Manager job openings in McHenry, IL as of June 2026, with employment types broken down into 71% Full Time, 26% Part Time, 1% Temporary, and 2% Contract. Highlights an 90% Physical, 2% Hybrid, and 8% Remote job distribution, with an average salary of $117,745 per year, or $56.6 per hour.

Reinforcement Learning Environment Engineer (Contract)

Cobalt

Mundelein, IL • On-site

Other

Posted 5 days ago

Job description

About the role:

Cobalt is seeking people who can build the environments frontier labs train and evaluate agents in: tasks with real difficulty, unambiguous success conditions, and scoring that survives contact with a capable model.

This opportunity is suited to reinforcement learning researchers, research engineers, simulation and tooling engineers, and people who have built serious benchmarks, competition problems, or training environments. Depth in RL is valuable, but so is the engineering discipline required to make an environment reproducible and hard to game.

You do not need prior experience in data annotation. What matters is that you can take a domain, decide what a meaningful task in it looks like, and build something that measures it correctly.


What you'll do:

Depending on the project, you may:

  • Design and build task environments with programmatic success criteria, including multi-step and tool-using tasks that cannot be solved by a shortcut
  • Specify reward functions and partial-credit schemes, and stress-test them for the ways a capable agent would exploit them rather than solve the task
  • Produce written reasoning traces and reference solutions showing how a competent human works through the tasks you build
  • Evaluate agent trajectories, identifying the specific step at which behavior goes wrong, and classifying failures into a consistent taxonomy
  • Assess whether a scored result reflects genuine task completion, and flag cases where the environment or the metric is measuring the wrong thing

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


Required qualifications:

  • Direct experience with reinforcement learning, agent evaluation, simulation, or benchmark and environment construction, whether in research, industry, or substantial open-source work
  • Strong software engineering ability in Python, sufficient to build reproducible environments, harnesses, and automated scoring
  • A PhD in a quantitative discipline, or equivalent depth demonstrated through published work, open-source contributions, or production systems
  • Understanding of reward hacking and specification gaming, and the instinct to look for them in your own designs before someone else does
  • Ability to explain each step of your reasoning and design decisions clearly in writing


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