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Full Time Machine Learning Data Annotation Jobs in Chicago, IL

IMC

Machine Learning Researcher

Chicago, IL · On-site

$250K - $300K/yr

Manage data acquisition, preprocessing, and feature engineering for structured and unstructured ... Base salary is only one component of total compensation; all full-time, permanent positions are ...

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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 Sep 3, 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 is a full time machine learning data annotation job?

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 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 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

Machine Learning Researcher

IMC

Chicago, IL • On-site

$250K - $300K/yr

Full-time

PTO

Re-posted 7 days ago

Key responsibilities

  • Design and deploy machine learning models to enhance trading performance across various asset classes

  • Research, test, and prototype new algorithmic ideas; deploy advanced ML techniques for market prediction, signal generation, and portfolio optimization

  • Manage data acquisition, preprocessing, and feature engineering for structured and unstructured data sources

Job description

IMC Trading is seeking quantitative researchers with a proven track record to apply state-of-the-art machine learning & deep learning to solve challenging trading problems. This role is part of a central ML research team that collaborates across trading teams at IMC. The ideal candidate will have experience working with other researchers and engineers to build and continuously improve models, systems, and research tooling. We firmly believe that success for research-driven efforts lies in bringing together skills in ML, statistics, and trading intuition as well as a problem-solving mindset and pragmatism. This is an opportunity to dive deep into feature engineering and alpha research, and focus on applying a wide range of ML models as well as to perform research on building custom models.
Your Core Responsibilities:
  • Design and deploy machine learning models to enhance trading performance across various asset classes
  • Research, test and prototype new algorithmic ideas; deploy advanced ML techniques applicable to market prediction, signal generation, and portfolio optimization
  • Collaborate with quantitative traders, researchers, and developers to translate market insights into data-driven features and models
  • Manage data acquisition, preprocessing, and feature engineering for structured and unstructured data sources

Your Skills and Experience:
  • PhD or Master's in Engineering, Math, Statistics, Computer Science, or related quantitative field
  • 2+ years of experience building applied ML models; previous experience in trading environment preferred
  • Proven expertise in developing and deploying predictive models
  • Strong programming skills in Python; proficiency in ML libraries such as PyTorch, TensorFlow, and/or high-performance libraries like Jax
  • Strong understanding of theoretical foundations of state-of-the-art ML models
  • Strong publication track record at ICML, ICLR, NeurIPS, or equivalent
  • Ability and desire to work in a collaborative team environment
  • Excellent written and verbal communication skills

The Base Salary range for the role is included below. Base salary is only one component of total compensation; all full-time, permanent positions are eligible for a discretionary bonus and benefits, including paid leave and insurance. Please visit Benefits - US | IMC Trading for more comprehensive information.
Salary Range
$250,000-$300,000 USD
About Us
IMC is a research-driven trading firm where quantitative modeling, machine learning, and engineering shape how modern markets are traded. A stabilizing force in markets since 1989, we provide liquidity across trading venues, delivering the best outcome in value and risk management to investors. Using our own technology and capital, we build proprietary systems and algorithms that operate across global markets. Our researchers, traders, and engineers work as a collective, combining rapid experimentation, advanced infrastructure, and real-time feedback to turn insight into execution and execution into advantage.

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