Senior Machine Learning (ML) Engineer (CCC Casualty / Claims Intelligence)

EvolutionIQ•Chicago, IL

About The Position

CCC Intelligent Solutions Inc. is hiring a Senior Machine Learning Engineer for a new, rapidly growing engineering group building AI-native products for its auto casualty business, focused on the injury side of auto claims. The group is small and senior, and works on products for medical bills, claims decision support, and an Agentic Rules Platform that supports decisions across casualty products and provides evaluation infrastructure. The engineer will own models and evaluations end to end within a problem area, using evidence to determine whether AI works and is valuable. The role combines statistical modeling, machine learning, and large language model evaluation, along with production engineering to ship models and evaluations. The position is bonus eligible, with a salary range of $180,000–$225,000.

Requirements

Typically 5 or more years of experience in machine learning, data science, or quantitative engineering roles.
A strong quantitative foundation in statistics, probability, and experiment design, gained through a degree, research, or equivalent experience.
Strong Python and SQL skills.
A track record of taking models to production and measuring their performance.
Hands-on experience with both statistical machine learning and LLM-based approaches, including RAG, embeddings, and vector search.
Experience evaluating LLM outputs.
Real experience working faster with AI-assisted tools, with examples the candidate can discuss.

Nice To Haves

Experience applying causal inference, survival analysis, or calibration methods to business decisions.
Experience fine-tuning models for domain-specific tasks.

Responsibilities

Build and ship statistical, machine learning, and hybrid LLM models, including retrieval and embeddings, using real claims data.
Design evaluations and gold datasets in collaboration with data labelers and subject matter experts, and run evaluations continuously as products change.
Measure product impact through experiment design, baselines, and ROI analysis.
Clearly explain results to product, sales, and customers.
Own models and evaluations end to end within a problem area and use the results to inform product decisions.