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

The Role

As a Senior Machine Learning Engineer, you'll own models and evaluations end to end within a problem area and turn the results into product decisions. You'll join a new engineering group at CCC building AI-native products for our auto casualty business, the injury side of auto claims. The group is small and senior, and it's growing fast.

Our teams work on:

• Medical bills: AI products that help claims teams handle the medical bills on injury claims accurately and efficiently.
• Claims decision support: products that help claims professionals make better, faster decisions on injury claims.
• Agentic Rules Platform: the rules engine and agentic tooling behind decisions across our casualty products, plus the evaluation infrastructure every team relies on.

Your impact:

You'll show, with evidence, whether our AI works and whether it's worth it. You'll build models on real claims data, design the evaluations that compare our AI with expert decisions, and measure the impact customers see. Most of our products run on large language models, so you'll combine statistical modeling with LLM evaluation and write the production code to ship both.

Key Responsibilities:

• Build and ship statistical, ML, and hybrid LLM models (including retrieval and embeddings) on real claims data.
• Design evaluations and gold datasets with data labelers and subject matter experts, and run them continuously as the product changes.
• Measure impact through experiment design, baselines, and ROI analysis, and explain the results clearly to product, sales, and customers.

What success looks like:

In your first month, you've run your first evaluation on real data and shared what you found. By six months, you've shipped a model or evaluation to production, and a product decision was made on evidence you produced.

Requirements:

• Typically 5+ years of experience in machine learning, data science, or quantitative engineering roles.
• A strong quantitative foundation (statistics, probability, experiment design) from a degree, research, or equivalent experience.
• Strong Python and SQL, and a track record of taking models to production and measuring how they perform.
• Hands-on experience with both statistical ML and LLM-based approaches (RAG, embeddings, vector search), including evaluating LLM outputs.
• Real experience working faster with AI-assisted tools, with examples you can walk us through.

Nice to have:

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