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Full Time Machine Learning Data Annotation Jobs in Chicago, IL

Coinflow

Machine Learning Lead

Chicago, IL · On-site

$175K - $235K/yr

That means digging into large-scale transaction and behavioral data, shipping production fraud ... in machine learning, applied data science, or production ML roles * Demonstrated experience ...

IMC

Hardware Machine Learning Engineer

Chicago, IL · On-site

$200K - $225K/yr

... physics data acquisition, real-time signal processing, or similar domains * Familiarity with ... Base salary is only one component of total compensation; all full-time, permanent positions are ...

The Hartford

Senior AI Machine Learning Engineer

Chicago, IL · Hybrid

$126K - $166K/yr

Sr Data Engineer - GE07BE We're determined to make a difference and are proud to be an insurance ... As a Senior Machine Learning Engineer , you will play a critical role in designing, building, and ...

Career Renew

Senior Machine Learning Engineer

Chicago, IL · Remote

$165K - $225K/yr

Career Renew is recruiting for one of its clients a Senior Machine Learning Engineer - this is a ... data scientists, and pathology domain experts to integrate research into production systems ...

Arrive Logistics

Data Scientist II

Chicago, IL · On-site

Experience designing data annotation workflows, labeling guidelines, or label quality processes is ... Build relationships and take part in learning opportunities through our Employee Resource Groups.

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All JobsMachine Learning Data Annotation JobsFull Time Machine Learning Data Annotation JobsFull Time Machine Learning Data Annotation Jobs in Chicago, IL

Full Time Machine Learning Data Annotation information

See Chicago, IL salary details

$38.6K

$126.4K

$202.4K

How much do full time machine learning data annotation jobs pay per year?

As of Jul 25, 2026, the average yearly pay for full time machine learning data annotation in Chicago, IL is $126,438.00, according to ZipRecruiter salary data. Most workers in this role earn between $101,500.00 and $140,100.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Full Time Machine Learning Data Annotation Specialist, and why are they important?

To thrive as a Full Time Machine Learning Data Annotation Specialist, you need strong attention to detail, basic data literacy, and familiarity with data labeling concepts, often supported by a high school diploma or equivalent. Proficiency in specialized annotation platforms, spreadsheet tools, and sometimes knowledge of Python or labeling frameworks is typically required. Reliability, patience, and effective communication are valuable soft skills for ensuring accuracy and collaborating with team members. These skills and qualities are crucial because they directly impact the quality of training data, which is essential for developing effective machine learning models.

What are Full Time Machine Learning Data Annotation jobs?

Full time machine learning data annotation jobs involve labeling, tagging, or categorizing data such as images, text, audio, or video to help train machine learning models. Data annotators play a crucial role in ensuring that AI systems learn from high-quality, accurately labeled datasets. These positions often require attention to detail, consistency, and sometimes familiarity with the subject matter or specialized tools. Full-time roles may be remote or onsite and can span industries like autonomous vehicles, healthcare, retail, and more.

What are some common challenges faced by machine learning data annotators, and how are these typically addressed within a team?

Machine learning data annotators often encounter challenges such as maintaining consistency in labeling, handling ambiguous data, and meeting tight deadlines for large datasets. Teams usually address these by establishing clear annotation guidelines, conducting regular training sessions, and implementing quality assurance processes like peer reviews and spot checks. Collaboration with data scientists and project managers is also common, ensuring that annotators can ask questions and clarify uncertainties, leading to higher-quality labeled data and a supportive work environment.

What is the difference between Full Time Machine Learning Data Annotation vs Data Labeling Specialist?

AspectFull Time Machine Learning Data AnnotationData Labeling Specialist
CredentialsHigh school diploma or equivalent; some roles prefer technical certificationsHigh school diploma or equivalent; training often provided on the job
Work EnvironmentOffice or remote; collaborative with data science teamsRemote or office; focused on labeling tasks
Industry UsageUsed across AI/ML companies, tech firms, and startupsCommon in AI/ML, data services, and outsourcing companies
Job FocusCreating labeled datasets for machine learning modelsAnnotating data such as images, videos, or text for AI training

Full Time Machine Learning Data Annotation involves creating high-quality labeled datasets for AI models, often requiring technical understanding. Data Labeling Specialists focus on annotating data accurately, typically with less emphasis on technical skills. Both roles are essential in AI development but differ mainly in scope and technical complexity.

What are the most commonly searched types of Machine Learning Data Annotation jobs in Chicago, IL? The most popular types of Machine Learning Data Annotation jobs in Chicago, IL are:
What are popular job titles related to Full Time Machine Learning Data Annotation jobs in Chicago, IL? For Full Time Machine Learning Data Annotation jobs in Chicago, IL, the most frequently searched job titles are:
What job categories do people searching Full Time Machine Learning Data Annotation jobs in Chicago, IL look for? The top searched job categories for Full Time Machine Learning Data Annotation jobs in Chicago, IL are:
Full Time Machine Learning Data Annotation jobs near you
Senior Software Engineer (Machine Learning)

Senior Software Engineer (Machine Learning)

Valor Equity Partners

Chicago, IL • On-site

$126K - $166K/yr

Full-time

Posted 5 days ago

Job description

About Valor:
Valor Equity Partners is a different kind of private investment firm. We pioneered the idea of operational growth. We work side-by-side, shoulder-to-shoulder, to help grow the operations of great companies solving the world's biggest problems. We invest in technology and technology-enabled companies that innovate and disrupt existing industries - from biosciences to transportation to food to health and wellness. We've had the honor of serving some of the world's greatest entrepreneurs and companies, including Tesla, SpaceX, Anduril, Eight Sleep, GoPuff, and others.
Our values are core to all we do. These values are excellence, humility, integrity, and responsibility.
Valor means that we:
  • Strive for excellence in everything we do;
  • Maintain our humility and mutual respect no matter what circumstances we encounter;
  • Insist upon the highest level of integrity in our interactions and in the logic of our investment process; and
  • Demonstrate responsibility and dedication to all of our constituents.

About the Team:
On the Valor Labs Team, we develop cutting edge machine learning models to derive proprietary investment insights and build software applications to augment the Firm's investment decision making process. As a small team of software engineers and data scientists with diverse backgrounds, we work collaboratively on wide-ranging problems to deliver high-impact products for the Firm.
About the Role:
As a Software Engineer on our data science and machine learning team, you will contribute directly to the development of high-impact products. Working together with data scientists, engineers, and stakeholders, you will translate complex project requirements into actionable technical solutions and work collaboratively to build, deploy, monitor, and maintain those solutions in production. Your technical expertise and commitment to excellence will help drive the adoption of best practices and ensure the highest level of rigor in everything we do.
About You:
  • B.S. in Computer Science or related field
  • 5+ years of experience developing production-ready software systems
    • Although not necessary, prior work experience in financial services is highly valued
  • Expertise in end-to-end machine learning operations: model deployment, monitoring, and retraining, supporting integration with production data pipelines and API services.
  • Proficient with Python, especially machine learning libraries like NumPy, Pandas, Scikit-Learn, and PyTorch
  • Proficient with SQL, including transactional (e.g., PostgreSQL) and analytical (e.g., BigQuery) databases
  • Professional experience with most, if not all, of the following:
    • Containerization (e.g., Kubernetes and Docker)
    • Data processing (e.g., Prefect, Airflow, and dbt)
    • Parallel processing (e.g., Ray, Dask, and Spark)
    • Cloud infrastructure (e.g., Google Cloud Platform)
    • Continuous integration/continuous deployment (e.g. GitHub Actions)
    • Infrastructure as code (e.g., Terraform)
    • Tools to support machine learning operations (e.g., MLFlow and DVC)
  • Humble, hard-working, and collaborative

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