Senior ML Engineer, AI for Auto Claims & Injury

CCC Intelligent Solutions Inc.; United States; Remote; USD 180,000 - 225,000; Full time

Benefits offered by this job

401K Match

Paid time off

Annual Incentive Plan

Performance Bonus

Comprehensive health insurance

Adoption Assistance

Tuition Reimbursement

Wellness Programs

Stock Purchase Plan options

Employee Resource Groups

Job summary

CCC Intelligent Solutions Inc. is seeking a Senior Machine Learning Engineer to own models and evaluations end-to-end for AI-native auto casualty products.

You’ll work on medical bills AI, claims decision support, and an agentic rules platform within a senior, growth-focused team. You will build and evaluate models with real claims data, using LLMs and traditional ML, and translate results into product decisions while ensuring production-grade deployments.

Qualifications

• 5+ years of experience in ML, data science, or quantitative engineering roles.
• Strong Python and SQL, with a track record of taking models to production and measuring performance.
• Hands-on experience with statistical ML and LLM-based approaches (RAG, embeddings, vector search).

Responsibilities

• Build and ship statistical, ML, and hybrid LLM models on real claims data.
• Design evaluations and gold datasets with data labelers and SMEs, and run them continuously as the product evolves.
• Measure impact through experiment design, baselines, ROI analysis, and communicate results to product, sales, and customers.

Skills

Python

SQL

Production deployment

LLM evaluation

RAG / embeddings

Experiment design

Statistics

Education

BS/MS in Computer Science, Math, or related field

Job description

CCC Intelligent Solutions Inc. is seeking a Senior Machine Learning Engineer to own models and evaluations end-to-end for AI-native auto casualty products.

You’ll work on medical bills AI, claims decision support, and an agentic rules platform within a senior, growth-focused team. You will build and evaluate models with real claims data, using LLMs and traditional ML, and translate results into product decisions while ensuring production-grade deployments.