Senior Machine Learning Engineer (Casualty / Claims Intelligence)

CCC Intelligent Solutions•Chicago, IL

5d•$180,000 - $225,000

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 works on AI products that help claims teams process medical bills accurately and efficiently, decision-support products for claims professionals, and an Agentic Rules Platform that powers decisions across casualty products and provides shared evaluation infrastructure. The engineer will own models and evaluations end to end within a problem area and use evidence to inform product decisions. The role involves building models on real claims data, designing evaluations that compare AI with expert decisions, and measuring customer impact. Most products use large language models, so the role combines statistical modeling, LLM evaluation, and production software engineering. Success includes running and sharing an evaluation on real data within the first month, and shipping a model or evaluation to production and informing a product decision with evidence within six months. The position is bonus eligible, with a stated 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 statistical machine learning and LLM-based approaches, including RAG, embeddings, vector search, and evaluating LLM outputs.
Real experience working faster with AI-assisted tools, with examples the candidate can explain.

Nice To Haves

Causal inference, survival analysis, or calibration methods applied 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 impact through experiment design, baselines, and ROI analysis, and clearly explain results to product teams, sales, and customers.
Own models and evaluations end to end within a problem area and translate findings into product decisions.