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

EvolutionIQ•Chicago, IL

3d•$180,000 - $225,000

About The Position

CCC Intelligent Solutions is hiring a Senior Machine Learning Engineer for its auto casualty and claims intelligence work. The role is part of a growing, small, senior engineering group building AI-native products for the injury side of auto claims. Product areas include AI tools for processing medical bills, claims decision support, and an agentic rules platform that supports decisions and evaluation infrastructure across casualty products. The engineer will own models and evaluations end to end within a problem area and turn evidence into product decisions. The work involves building models with real claims data, designing evaluations that compare AI results with expert decisions, measuring customer impact, and writing production code to ship statistical models and large-language-model-based products. Success includes running an evaluation on real data and sharing findings within the first month, then 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.
Strong quantitative foundation in statistics, probability, and experiment design, acquired through a degree, research, or equivalent experience.
Strong Python and SQL skills.
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 to 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 with data labelers and subject matter experts, and run evaluations continuously as products change.
Measure impact using experiment design, baselines, and ROI analysis.
Explain results clearly to product teams, sales, and customers.
Own models and evaluations end to end within a problem area and use results to inform product decisions.
Write production code to ship models and evaluations.