[Hiring] Senior Machine Learning Engineer @CCC Intelligent Solutions

Role Description

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.

Salary range is: $180,000 - 225,000. This position is bonus eligible.

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.

Qualifications

• 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.

Requirements

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

Benefits

• 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