Machine Learning - Annotation and Gen AI Analyst Intern

CCC Intelligent Solutions•Chicago, IL

1d•$20 - $43

About The Position

CCC Intelligent Solutions Inc. (CCC) is a leading cloud platform for the multi-trillion-dollar insurance economy, creating intelligent experiences for insurers, repairers, automakers, part suppliers, and more. At CCC, we’re making life just work by empowering more than 35,000 businesses with industry-leading technology to get drivers back on the road and to health quickly and seamlessly. We’re pushing boundaries with innovative AI solutions that simplify and enhance the claims and repair journey. Through purposeful innovation and the strength of its connections, CCC technologies empower the people and industry relied upon to keep lives moving forward when it matters most. Learn more about CCC at www.cccis.com. The Role Our program is designed to #CCCJumpstart your career. At CCC you will work and learn alongside innovative leaders and gain hands-on experience with annotation pipelines, quality analysis, and Gen AI products in a corporate setting.

Requirements

Strong quantitative or analytical background (e.g., degree in computer science, math, statistics, engineering, or related field) and solid programming skills, preferably in Python.
Comfort with Git, Jira, and cloud (e.g., AWS) and the ability to drive clear documentation and process improvements are important.

Nice To Haves

Experience with data validation, quality metrics, or annotation workflows is a plus.

Responsibilities

Annotation data consolidation and validation: Own pipelines to aggregate, reconcile, and validate annotation outputs from a team of 4–6 annotators; define and enforce schemas, rules, and consistency checks.
Quality analysis: Design and run quality metrics (e.g., inter-annotator agreement, error rates, coverage); identify systematic issues and recommend process and guideline improvements.
Gen AI product involvement: Support Gen AI initiatives by curating and validating training or evaluation data, defining quality criteria, and contributing to evaluation and monitoring.
Documentation and process: Raise standards in documentation, runbooks, and workflows; improve traceability and reproducibility of annotation and validation steps.