Applied ML Engineer

Role Overview

• Data Platform Architecture: Build a claims data platform from scratch including ingestion pipelines, schema design, feature engineering, storage solutions, and orchestration systems designed for scalability.
• Production ML Deployment: Ship the first estimation models to production environments, iterating continuously against live claim outcomes in real-world scenarios.
• Data Strategy & Labeling: Design rigorous labeling strategies where labels represent negotiated numbers rather than ground truth. Develop evaluation frameworks that account for negotiation dynamics between contractors and carriers.
• Integration & Tooling: Wire model outputs directly into live agent workflows supporting real-time claim processing. Build comprehensive tooling (training pipelines, experiment tracking, serving infrastructure, monitoring) enabling a small team to execute the capabilities of ten.

Nice-to-Have Skills

• Structured extraction from documents, photos, or call transcripts using computer vision/NLP techniques.
• Experience with insurance domain data: pricing models, risk assessment frameworks, and marketplace dynamics.
• LLM engineering expertise for document/photo transcription and intelligent agent tool orchestration.