Chubb AI Engineer Interview Guide

Candidates describe Chubb's AI Engineer interviews as a mix of conceptual machine learning questions and applied design discussion, with varying structure from one person to the next. One candidate reports a screening call that moved from behavioral and logistical questions into transformers, LLMs, and RLHF, and says their limited hands-on exposure showed. Another reports four rounds that were entirely theoretical, with no practical questions. Topics included positional embeddings, transformers versus RNNs, temperature and top-k sampling, correlation versus covariance, and agent workflows. Question difficulty in that account ranged from backpropagation to basic Python list-versus-dictionary differences.

A third candidate describes a phone screen, a hiring-manager conversation about LLM and agentic experience, and an emailed case study: design an agentic system for insurance claim submission and processing. They presented an architecture diagram and were told they fit the AI Engineer level better than Senior because of limited production deployment experience. A fourth candidate describes a final technical round that included typing code live, with questions that did not closely match the job description.

For preparation, review core LLM concepts well enough to explain them aloud. Sketch an agentic claims workflow with clear components and failure handling. Prepare concrete stories about systems you have run in production, such as monitoring and deployment. Several candidates mention slow replies, so confirm timelines and any flexibility in writing. All four reports here ended without an accepted offer.