Applied ML Engineer

Every claim we process generates hundreds of structured data points: line items, quantities, unit prices, room geometry, equipment counts, what was submitted and what the carrier approved. Years of this history sit in reports and carrier portals that nobody has ever built models on. Your job is to turn that raw sprawl into a data asset and ship the first models on top of it.

The pipeline is the hard part and the moat. Reports are messy, schemas drift across franchise locations, carrier responses arrive in half a dozen formats and the labels are negotiated outcomes rather than ground truth. The engineer who wins this role treats that mess as the job: builds ingestion that survives it, designs the schema the whole company will stand on and then trains the models, starting with gradient-boosted tabular systems predicting line-item approval outcomes and going wherever the data leads. Scope prediction from photos and transcripts, leakage detection and pricing intelligence are all open and unbuilt.