Senior Machine Learning Engineer, EvolutionIQ

• 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

Desirable

• 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
• 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
• 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

• A video interview is required for this position. Video interviews are transcribed. Transcriptions are retained and may be reviewed by CCC and our recruiters
• Candidates are not permitted to use generative AI or automated assistance during the interviews unless explicitly allowed by the interview team for a specific exercise